Overview

Dataset statistics

Number of variables31
Number of observations1000
Missing cells3365
Missing cells (%)10.9%
Total size in memory7.8 MiB
Average record size in memory8.0 KiB

Variable types

Numeric1
Text29
Unsupported1

Alerts

process_date has constant value ""Constant
full_time_part_time_indicator has 26 (2.6%) missing valuesMissing
preferred_skills has 194 (19.4%) missing valuesMissing
additional_information has 278 (27.8%) missing valuesMissing
hours_shift has 606 (60.6%) missing valuesMissing
work_location_1 has 585 (58.5%) missing valuesMissing
recruitment_contact has 1000 (100.0%) missing valuesMissing
post_until has 669 (66.9%) missing valuesMissing
0 has unique valuesUnique
recruitment_contact is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-09 20:31:12.890825
Analysis finished2023-12-09 20:31:15.685139
Duration2.79 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

0
Real number (ℝ)

UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.5
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-12-09T20:31:16.042023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50.95
Q1250.75
median500.5
Q3750.25
95-th percentile950.05
Maximum1000
Range999
Interquartile range (IQR)499.5

Descriptive statistics

Standard deviation288.8194361
Coefficient of variation (CV)0.5770618104
Kurtosis-1.2
Mean500.5
Median Absolute Deviation (MAD)250
Skewness0
Sum500500
Variance83416.66667
MonotonicityStrictly increasing
2023-12-09T20:31:16.198102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
672 1
 
0.1%
659 1
 
0.1%
660 1
 
0.1%
661 1
 
0.1%
662 1
 
0.1%
663 1
 
0.1%
664 1
 
0.1%
665 1
 
0.1%
666 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
ValueCountFrequency (%)
1000 1
0.1%
999 1
0.1%
998 1
0.1%
997 1
0.1%
996 1
0.1%

job_id
Text

Distinct914
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Memory size61.6 KiB
2023-12-09T20:31:16.624443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6000
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique831 ?
Unique (%)83.1%

Sample

1st row592901
2nd row595589
3rd row587575
4th row589409
5th row598782
ValueCountFrequency (%)
591278 3
 
0.3%
615137 3
 
0.3%
535215 3
 
0.3%
596285 2
 
0.2%
545815 2
 
0.2%
580604 2
 
0.2%
609164 2
 
0.2%
581073 2
 
0.2%
583761 2
 
0.2%
540908 2
 
0.2%
Other values (904) 977
97.7%
2023-12-09T20:31:17.175207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 1021
17.0%
6 828
13.8%
0 680
11.3%
9 614
10.2%
8 588
9.8%
1 560
9.3%
3 453
7.5%
7 451
7.5%
2 420
7.0%
4 385
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1021
17.0%
6 828
13.8%
0 680
11.3%
9 614
10.2%
8 588
9.8%
1 560
9.3%
3 453
7.5%
7 451
7.5%
2 420
7.0%
4 385
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 6000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 1021
17.0%
6 828
13.8%
0 680
11.3%
9 614
10.2%
8 588
9.8%
1 560
9.3%
3 453
7.5%
7 451
7.5%
2 420
7.0%
4 385
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 1021
17.0%
6 828
13.8%
0 680
11.3%
9 614
10.2%
8 588
9.8%
1 560
9.3%
3 453
7.5%
7 451
7.5%
2 420
7.0%
4 385
 
6.4%

agency
Text

Distinct47
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size81.9 KiB
2023-12-09T20:31:17.489363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length30
Median length28
Mean length26.787
Min length14

Characters and Unicode

Total characters26787
Distinct characters35
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.9%

Sample

1st rowTAXI & LIMOUSINE COMMISSION
2nd rowDEPT OF HEALTH/MENTAL HYGIENE
3rd rowHRA/DEPT OF SOCIAL SERVICES
4th rowOFF OF PAYROLL ADMINISTRATION
5th rowDEPT OF ENVIRONMENT PROTECTION
ValueCountFrequency (%)
of 749
19.4%
dept 484
 
12.5%
health/mental 190
 
4.9%
hygiene 190
 
4.9%
department 163
 
4.2%
protection 155
 
4.0%
155
 
4.0%
environment 149
 
3.9%
services 126
 
3.3%
hra/dept 109
 
2.8%
Other values (91) 1391
36.0%
2023-12-09T20:31:17.931655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 2929
 
10.9%
2861
 
10.7%
T 2634
 
9.8%
O 2204
 
8.2%
N 2145
 
8.0%
I 1617
 
6.0%
R 1373
 
5.1%
A 1345
 
5.0%
P 1078
 
4.0%
S 1025
 
3.8%
Other values (25) 7576
28.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 23434
87.5%
Space Separator 2861
 
10.7%
Other Punctuation 487
 
1.8%
Decimal Number 3
 
< 0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 2929
12.5%
T 2634
11.2%
O 2204
 
9.4%
N 2145
 
9.2%
I 1617
 
6.9%
R 1373
 
5.9%
A 1345
 
5.7%
P 1078
 
4.6%
S 1025
 
4.4%
D 1021
 
4.4%
Other values (15) 6063
25.9%
Other Punctuation
ValueCountFrequency (%)
/ 299
61.4%
& 155
31.8%
. 17
 
3.5%
' 14
 
2.9%
# 2
 
0.4%
Decimal Number
ValueCountFrequency (%)
1 1
33.3%
0 1
33.3%
7 1
33.3%
Space Separator
ValueCountFrequency (%)
2861
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23434
87.5%
Common 3353
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 2929
12.5%
T 2634
11.2%
O 2204
 
9.4%
N 2145
 
9.2%
I 1617
 
6.9%
R 1373
 
5.9%
A 1345
 
5.7%
P 1078
 
4.6%
S 1025
 
4.4%
D 1021
 
4.4%
Other values (15) 6063
25.9%
Common
ValueCountFrequency (%)
2861
85.3%
/ 299
 
8.9%
& 155
 
4.6%
. 17
 
0.5%
' 14
 
0.4%
- 2
 
0.1%
# 2
 
0.1%
1 1
 
< 0.1%
0 1
 
< 0.1%
7 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26787
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 2929
 
10.9%
2861
 
10.7%
T 2634
 
9.8%
O 2204
 
8.2%
N 2145
 
8.0%
I 1617
 
6.0%
R 1373
 
5.1%
A 1345
 
5.0%
P 1078
 
4.0%
S 1025
 
3.8%
Other values (25) 7576
28.3%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
2023-12-09T20:31:18.099716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8000
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInternal
2nd rowExternal
3rd rowExternal
4th rowInternal
5th rowExternal
ValueCountFrequency (%)
external 502
50.2%
internal 498
49.8%
2023-12-09T20:31:18.367197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 1498
18.7%
t 1000
12.5%
e 1000
12.5%
r 1000
12.5%
a 1000
12.5%
l 1000
12.5%
E 502
 
6.3%
x 502
 
6.3%
I 498
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7000
87.5%
Uppercase Letter 1000
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1498
21.4%
t 1000
14.3%
e 1000
14.3%
r 1000
14.3%
a 1000
14.3%
l 1000
14.3%
x 502
 
7.2%
Uppercase Letter
ValueCountFrequency (%)
E 502
50.2%
I 498
49.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 8000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1498
18.7%
t 1000
12.5%
e 1000
12.5%
r 1000
12.5%
a 1000
12.5%
l 1000
12.5%
E 502
 
6.3%
x 502
 
6.3%
I 498
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1498
18.7%
t 1000
12.5%
e 1000
12.5%
r 1000
12.5%
a 1000
12.5%
l 1000
12.5%
E 502
 
6.3%
x 502
 
6.3%
I 498
 
6.2%
Distinct20
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
2023-12-09T20:31:18.497902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length2
Median length1
Mean length1.024
Min length1

Characters and Unicode

Total characters1024
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.7%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row2
ValueCountFrequency (%)
1 819
81.9%
2 81
 
8.1%
3 30
 
3.0%
4 17
 
1.7%
5 14
 
1.4%
10 8
 
0.8%
7 5
 
0.5%
15 4
 
0.4%
6 4
 
0.4%
9 3
 
0.3%
Other values (10) 15
 
1.5%
2023-12-09T20:31:18.740757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 839
81.9%
2 84
 
8.2%
3 31
 
3.0%
5 22
 
2.1%
4 18
 
1.8%
0 12
 
1.2%
7 7
 
0.7%
6 5
 
0.5%
8 3
 
0.3%
9 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1024
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 839
81.9%
2 84
 
8.2%
3 31
 
3.0%
5 22
 
2.1%
4 18
 
1.8%
0 12
 
1.2%
7 7
 
0.7%
6 5
 
0.5%
8 3
 
0.3%
9 3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1024
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 839
81.9%
2 84
 
8.2%
3 31
 
3.0%
5 22
 
2.1%
4 18
 
1.8%
0 12
 
1.2%
7 7
 
0.7%
6 5
 
0.5%
8 3
 
0.3%
9 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1024
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 839
81.9%
2 84
 
8.2%
3 31
 
3.0%
5 22
 
2.1%
4 18
 
1.8%
0 12
 
1.2%
7 7
 
0.7%
6 5
 
0.5%
8 3
 
0.3%
9 3
 
0.3%
Distinct776
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
2023-12-09T20:31:19.122787image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length150
Median length78
Mean length32.75
Min length4

Characters and Unicode

Total characters32750
Distinct characters74
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique642 ?
Unique (%)64.2%

Sample

1st rowProgram Coordinator, Office of Financial Stability
2nd rowHIV Clinical Technical Assistance Specialist, Bureau of Hepatitis, HIV, and STI
3rd rowADMINSTRATIVE ASSISTANT TO THE DIRECTOR
4th rowHelp Desk Level 1 Representative
5th rowCHIEF MARINE ENGINEER (DIESEL)
ValueCountFrequency (%)
of 221
 
5.2%
bureau 146
 
3.5%
manager 110
 
2.6%
and 106
 
2.5%
director 93
 
2.2%
project 83
 
2.0%
health 82
 
1.9%
assistant 81
 
1.9%
analyst 80
 
1.9%
specialist 77
 
1.8%
Other values (774) 3134
74.4%
2023-12-09T20:31:19.696186image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3240
 
9.9%
e 2421
 
7.4%
i 1984
 
6.1%
a 1921
 
5.9%
t 1812
 
5.5%
r 1803
 
5.5%
n 1769
 
5.4%
o 1584
 
4.8%
s 1164
 
3.6%
l 887
 
2.7%
Other values (64) 14165
43.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20295
62.0%
Uppercase Letter 8578
26.2%
Space Separator 3240
 
9.9%
Other Punctuation 327
 
1.0%
Dash Punctuation 103
 
0.3%
Open Punctuation 70
 
0.2%
Close Punctuation 70
 
0.2%
Decimal Number 53
 
0.2%
Control 14
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2421
11.9%
i 1984
9.8%
a 1921
9.5%
t 1812
8.9%
r 1803
8.9%
n 1769
8.7%
o 1584
 
7.8%
s 1164
 
5.7%
l 887
 
4.4%
c 874
 
4.3%
Other values (17) 4076
20.1%
Uppercase Letter
ValueCountFrequency (%)
A 850
 
9.9%
E 813
 
9.5%
S 752
 
8.8%
C 708
 
8.3%
I 694
 
8.1%
T 570
 
6.6%
R 504
 
5.9%
P 499
 
5.8%
N 432
 
5.0%
O 413
 
4.8%
Other values (16) 2343
27.3%
Other Punctuation
ValueCountFrequency (%)
, 248
75.8%
/ 41
 
12.5%
& 21
 
6.4%
. 7
 
2.1%
' 5
 
1.5%
: 3
 
0.9%
@ 2
 
0.6%
Decimal Number
ValueCountFrequency (%)
1 24
45.3%
2 10
18.9%
3 10
18.9%
0 4
 
7.5%
4 3
 
5.7%
6 1
 
1.9%
5 1
 
1.9%
Control
ValueCountFrequency (%)
€ 7
50.0%
“ 6
42.9%
™ 1
 
7.1%
Space Separator
ValueCountFrequency (%)
3240
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 103
100.0%
Open Punctuation
ValueCountFrequency (%)
( 70
100.0%
Close Punctuation
ValueCountFrequency (%)
) 70
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 28873
88.2%
Common 3877
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2421
 
8.4%
i 1984
 
6.9%
a 1921
 
6.7%
t 1812
 
6.3%
r 1803
 
6.2%
n 1769
 
6.1%
o 1584
 
5.5%
s 1164
 
4.0%
l 887
 
3.1%
c 874
 
3.0%
Other values (43) 12654
43.8%
Common
ValueCountFrequency (%)
3240
83.6%
, 248
 
6.4%
- 103
 
2.7%
( 70
 
1.8%
) 70
 
1.8%
/ 41
 
1.1%
1 24
 
0.6%
& 21
 
0.5%
2 10
 
0.3%
3 10
 
0.3%
Other values (11) 40
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32729
99.9%
None 21
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3240
 
9.9%
e 2421
 
7.4%
i 1984
 
6.1%
a 1921
 
5.9%
t 1812
 
5.5%
r 1803
 
5.5%
n 1769
 
5.4%
o 1584
 
4.8%
s 1164
 
3.6%
l 887
 
2.7%
Other values (60) 14144
43.2%
None
ValueCountFrequency (%)
â 7
33.3%
€ 7
33.3%
“ 6
28.6%
™ 1
 
4.8%
Distinct231
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Memory size78.5 KiB
2023-12-09T20:31:20.042785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length32
Median length28
Mean length23.184
Min length4

Characters and Unicode

Total characters23184
Distinct characters50
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)10.0%

Sample

1st rowCOMMUNITY COORDINATOR
2nd rowCITY RESEARCH SCIENTIST
3rd rowASSOCIATE JOB OPPORTUNITY SPEC
4th rowCLERICAL ASSOCIATE
5th rowCHIEF MARINE ENGINEER (DIESEL)
ValueCountFrequency (%)
associate 147
 
5.2%
community 123
 
4.4%
city 117
 
4.1%
administrative 114
 
4.0%
research 87
 
3.1%
scientist 84
 
3.0%
specialist 81
 
2.9%
manager 80
 
2.8%
project 79
 
2.8%
analyst 76
 
2.7%
Other values (294) 1835
65.0%
2023-12-09T20:31:20.537607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 2188
9.4%
A 2080
 
9.0%
T 2079
 
9.0%
E 2043
 
8.8%
1825
 
7.9%
S 1708
 
7.4%
C 1582
 
6.8%
R 1500
 
6.5%
N 1483
 
6.4%
O 1289
 
5.6%
Other values (40) 5407
23.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 20947
90.4%
Space Separator 1825
 
7.9%
Open Punctuation 167
 
0.7%
Close Punctuation 99
 
0.4%
Dash Punctuation 50
 
0.2%
Decimal Number 46
 
0.2%
Other Punctuation 28
 
0.1%
Lowercase Letter 20
 
0.1%
Control 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 2188
10.4%
A 2080
9.9%
T 2079
9.9%
E 2043
9.8%
S 1708
8.2%
C 1582
 
7.6%
R 1500
 
7.2%
N 1483
 
7.1%
O 1289
 
6.2%
M 800
 
3.8%
Other values (14) 4195
20.0%
Lowercase Letter
ValueCountFrequency (%)
i 3
15.0%
c 2
10.0%
y 2
10.0%
a 2
10.0%
r 2
10.0%
n 2
10.0%
t 2
10.0%
o 1
 
5.0%
u 1
 
5.0%
e 1
 
5.0%
Other values (2) 2
10.0%
Other Punctuation
ValueCountFrequency (%)
/ 10
35.7%
& 8
28.6%
# 5
17.9%
, 3
 
10.7%
' 2
 
7.1%
Decimal Number
ValueCountFrequency (%)
1 18
39.1%
2 17
37.0%
3 11
23.9%
Control
ValueCountFrequency (%)
“ 1
50.0%
€ 1
50.0%
Space Separator
ValueCountFrequency (%)
1825
100.0%
Open Punctuation
ValueCountFrequency (%)
( 167
100.0%
Close Punctuation
ValueCountFrequency (%)
) 99
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20967
90.4%
Common 2217
 
9.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 2188
10.4%
A 2080
9.9%
T 2079
9.9%
E 2043
9.7%
S 1708
8.1%
C 1582
 
7.5%
R 1500
 
7.2%
N 1483
 
7.1%
O 1289
 
6.1%
M 800
 
3.8%
Other values (26) 4215
20.1%
Common
ValueCountFrequency (%)
1825
82.3%
( 167
 
7.5%
) 99
 
4.5%
- 50
 
2.3%
1 18
 
0.8%
2 17
 
0.8%
3 11
 
0.5%
/ 10
 
0.5%
& 8
 
0.4%
# 5
 
0.2%
Other values (4) 7
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23181
> 99.9%
None 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 2188
9.4%
A 2080
 
9.0%
T 2079
 
9.0%
E 2043
 
8.8%
1825
 
7.9%
S 1708
 
7.4%
C 1582
 
6.8%
R 1500
 
6.5%
N 1483
 
6.4%
O 1289
 
5.6%
Other values (37) 5404
23.3%
None
ValueCountFrequency (%)
“ 1
33.3%
€ 1
33.3%
â 1
33.3%
Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size70.2 KiB
2023-12-09T20:31:20.727554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length24
Median length13
Mean length14.746
Min length7

Characters and Unicode

Total characters14746
Distinct characters30
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNon-Competitive-5
2nd rowNon-Competitive-5
3rd rowCompetitive-1
4th rowCompetitive-1
5th rowCompetitive-1
ValueCountFrequency (%)
competitive-1 597
57.2%
non-competitive-5 342
32.8%
pending 43
 
4.1%
classification-2 43
 
4.1%
exempt-4 13
 
1.2%
labor-3 5
 
0.5%
2023-12-09T20:31:21.034097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 2050
13.9%
e 1934
13.1%
t 1934
13.1%
- 1342
9.1%
o 1329
9.0%
C 982
6.7%
m 952
6.5%
p 952
6.5%
v 939
6.4%
1 597
 
4.0%
Other values (20) 1735
11.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10976
74.4%
Uppercase Letter 1385
 
9.4%
Dash Punctuation 1342
 
9.1%
Decimal Number 1000
 
6.8%
Space Separator 43
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 2050
18.7%
e 1934
17.6%
t 1934
17.6%
o 1329
12.1%
m 952
8.7%
p 952
8.7%
v 939
8.6%
n 471
 
4.3%
a 91
 
0.8%
s 86
 
0.8%
Other values (8) 238
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
C 982
70.9%
N 342
 
24.7%
P 43
 
3.1%
E 13
 
0.9%
L 5
 
0.4%
Decimal Number
ValueCountFrequency (%)
1 597
59.7%
5 342
34.2%
2 43
 
4.3%
4 13
 
1.3%
3 5
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
- 1342
100.0%
Space Separator
ValueCountFrequency (%)
43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12361
83.8%
Common 2385
 
16.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 2050
16.6%
e 1934
15.6%
t 1934
15.6%
o 1329
10.8%
C 982
7.9%
m 952
7.7%
p 952
7.7%
v 939
7.6%
n 471
 
3.8%
N 342
 
2.8%
Other values (13) 476
 
3.9%
Common
ValueCountFrequency (%)
- 1342
56.3%
1 597
25.0%
5 342
 
14.3%
2 43
 
1.8%
43
 
1.8%
4 13
 
0.5%
3 5
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14746
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 2050
13.9%
e 1934
13.1%
t 1934
13.1%
- 1342
9.1%
o 1329
9.0%
C 982
6.7%
m 952
6.5%
p 952
6.5%
v 939
6.4%
1 597
 
4.0%
Other values (20) 1735
11.8%
Distinct243
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Memory size60.7 KiB
2023-12-09T20:31:21.426091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5000
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique111 ?
Unique (%)11.1%

Sample

1st row56058
2nd row21744
3rd row52316
4th row10251
5th row91523
ValueCountFrequency (%)
21744 81
 
8.1%
56058 73
 
7.3%
56057 40
 
4.0%
10124 32
 
3.2%
10251 30
 
3.0%
10209 21
 
2.1%
22427 19
 
1.9%
13632 18
 
1.8%
30087 17
 
1.7%
20215 16
 
1.6%
Other values (233) 653
65.3%
2023-12-09T20:31:21.944550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 943
18.9%
1 877
17.5%
2 753
15.1%
5 587
11.7%
6 399
8.0%
4 344
 
6.9%
3 290
 
5.8%
7 261
 
5.2%
8 259
 
5.2%
9 204
 
4.1%
Other values (6) 83
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4917
98.3%
Uppercase Letter 83
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 943
19.2%
1 877
17.8%
2 753
15.3%
5 587
11.9%
6 399
8.1%
4 344
 
7.0%
3 290
 
5.9%
7 261
 
5.3%
8 259
 
5.3%
9 204
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
A 37
44.6%
C 15
18.1%
B 14
 
16.9%
D 14
 
16.9%
F 2
 
2.4%
E 1
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 4917
98.3%
Latin 83
 
1.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 943
19.2%
1 877
17.8%
2 753
15.3%
5 587
11.9%
6 399
8.1%
4 344
 
7.0%
3 290
 
5.9%
7 261
 
5.3%
8 259
 
5.3%
9 204
 
4.1%
Latin
ValueCountFrequency (%)
A 37
44.6%
C 15
18.1%
B 14
 
16.9%
D 14
 
16.9%
F 2
 
2.4%
E 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 943
18.9%
1 877
17.5%
2 753
15.1%
5 587
11.7%
6 399
8.0%
4 344
 
6.9%
3 290
 
5.8%
7 261
 
5.2%
8 259
 
5.2%
9 204
 
4.1%
Other values (6) 83
 
1.7%

level
Text

Distinct17
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size57.7 KiB
2023-12-09T20:31:22.120701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2000
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row00
2nd row02
3rd row02
4th row02
5th row00
ValueCountFrequency (%)
00 392
39.2%
02 199
19.9%
01 164
16.4%
03 128
 
12.8%
m2 26
 
2.6%
m4 25
 
2.5%
m1 21
 
2.1%
m3 16
 
1.6%
04 12
 
1.2%
4a 5
 
0.5%
Other values (7) 12
 
1.2%
2023-12-09T20:31:22.393641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1287
64.3%
2 225
 
11.2%
1 187
 
9.3%
3 144
 
7.2%
M 96
 
4.8%
4 44
 
2.2%
A 5
 
0.2%
B 4
 
0.2%
8 2
 
0.1%
Y 2
 
0.1%
Other values (3) 4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1893
94.7%
Uppercase Letter 107
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1287
68.0%
2 225
 
11.9%
1 187
 
9.9%
3 144
 
7.6%
4 44
 
2.3%
8 2
 
0.1%
6 2
 
0.1%
5 1
 
0.1%
7 1
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
M 96
89.7%
A 5
 
4.7%
B 4
 
3.7%
Y 2
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1893
94.7%
Latin 107
 
5.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1287
68.0%
2 225
 
11.9%
1 187
 
9.9%
3 144
 
7.6%
4 44
 
2.3%
8 2
 
0.1%
6 2
 
0.1%
5 1
 
0.1%
7 1
 
0.1%
Latin
ValueCountFrequency (%)
M 96
89.7%
A 5
 
4.7%
B 4
 
3.7%
Y 2
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1287
64.3%
2 225
 
11.2%
1 187
 
9.3%
3 144
 
7.2%
M 96
 
4.8%
4 44
 
2.2%
A 5
 
0.2%
B 4
 
0.2%
8 2
 
0.1%
Y 2
 
0.1%
Other values (3) 4
 
0.2%
Distinct113
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Memory size99.7 KiB
2023-12-09T20:31:22.612570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length201
Median length141
Mean length44.97
Min length6

Characters and Unicode

Total characters44970
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)5.0%

Sample

1st rowConstituent Services & Community Programs Finance, Accounting, & Procurement Policy, Research & Analysis
2nd rowConstituent Services & Community Programs Health Policy, Research & Analysis
3rd rowAdministration & Human Resources Social Services
4th rowTechnology, Data & Innovation Policy, Research & Analysis
5th rowBuilding Operations & Maintenance
ValueCountFrequency (%)
1139
20.5%
services 347
 
6.2%
research 198
 
3.6%
analysis 198
 
3.6%
policy 198
 
3.6%
constituent 193
 
3.5%
programs 193
 
3.5%
community 193
 
3.5%
architecture 180
 
3.2%
engineering 180
 
3.2%
Other values (23) 2540
45.7%
2023-12-09T20:31:23.012620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4559
 
10.1%
n 4237
 
9.4%
e 3776
 
8.4%
i 3412
 
7.6%
t 2635
 
5.9%
a 2440
 
5.4%
c 2367
 
5.3%
r 2337
 
5.2%
o 2293
 
5.1%
s 2189
 
4.9%
Other values (28) 14725
32.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 33729
75.0%
Space Separator 4559
 
10.1%
Uppercase Letter 4420
 
9.8%
Other Punctuation 2262
 
5.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 4237
12.6%
e 3776
11.2%
i 3412
10.1%
t 2635
 
7.8%
a 2440
 
7.2%
c 2367
 
7.0%
r 2337
 
6.9%
o 2293
 
6.8%
s 2189
 
6.5%
l 1372
 
4.1%
Other values (10) 6671
19.8%
Uppercase Letter
ValueCountFrequency (%)
P 796
18.0%
A 732
16.6%
S 634
14.3%
C 445
10.1%
E 313
 
7.1%
I 307
 
6.9%
R 304
 
6.9%
H 281
 
6.4%
D 115
 
2.6%
T 115
 
2.6%
Other values (5) 378
8.6%
Other Punctuation
ValueCountFrequency (%)
& 1139
50.4%
, 1123
49.6%
Space Separator
ValueCountFrequency (%)
4559
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 38149
84.8%
Common 6821
 
15.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 4237
 
11.1%
e 3776
 
9.9%
i 3412
 
8.9%
t 2635
 
6.9%
a 2440
 
6.4%
c 2367
 
6.2%
r 2337
 
6.1%
o 2293
 
6.0%
s 2189
 
5.7%
l 1372
 
3.6%
Other values (25) 11091
29.1%
Common
ValueCountFrequency (%)
4559
66.8%
& 1139
 
16.7%
, 1123
 
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4559
 
10.1%
n 4237
 
9.4%
e 3776
 
8.4%
i 3412
 
7.6%
t 2635
 
5.9%
a 2440
 
5.4%
c 2367
 
5.3%
r 2337
 
5.2%
o 2293
 
5.1%
s 2189
 
4.9%
Other values (28) 14725
32.7%
Distinct2
Distinct (%)0.2%
Missing26
Missing (%)2.6%
Memory size56.1 KiB
2023-12-09T20:31:23.125989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters974
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowF
5th rowF
ValueCountFrequency (%)
f 930
95.5%
p 44
 
4.5%
2023-12-09T20:31:23.336407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
F 930
95.5%
P 44
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 974
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 930
95.5%
P 44
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 974
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 930
95.5%
P 44
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 974
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 930
95.5%
P 44
 
4.5%
Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size76.8 KiB
2023-12-09T20:31:23.509969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length25
Median length25
Mean length21.52
Min length7

Characters and Unicode

Total characters21520
Distinct characters25
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowExperienced (non-manager)
2nd rowExperienced (non-manager)
3rd rowExperienced (non-manager)
4th rowExperienced (non-manager)
5th rowExperienced (non-manager)
ValueCountFrequency (%)
experienced 787
44.0%
non-manager 787
44.0%
manager 101
 
5.7%
entry-level 85
 
4.8%
student 20
 
1.1%
executive 7
 
0.4%
2023-12-09T20:31:23.811266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 3453
16.0%
n 3354
15.6%
a 1776
 
8.3%
r 1760
 
8.2%
g 888
 
4.1%
E 879
 
4.1%
- 872
 
4.1%
d 807
 
3.8%
c 794
 
3.7%
i 794
 
3.7%
Other values (15) 6143
28.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 17202
79.9%
Uppercase Letter 1085
 
5.0%
Dash Punctuation 872
 
4.1%
Close Punctuation 787
 
3.7%
Open Punctuation 787
 
3.7%
Space Separator 787
 
3.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3453
20.1%
n 3354
19.5%
a 1776
10.3%
r 1760
10.2%
g 888
 
5.2%
d 807
 
4.7%
c 794
 
4.6%
i 794
 
4.6%
x 794
 
4.6%
m 787
 
4.6%
Other values (7) 1995
11.6%
Uppercase Letter
ValueCountFrequency (%)
E 879
81.0%
M 101
 
9.3%
L 85
 
7.8%
S 20
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
- 872
100.0%
Close Punctuation
ValueCountFrequency (%)
) 787
100.0%
Open Punctuation
ValueCountFrequency (%)
( 787
100.0%
Space Separator
ValueCountFrequency (%)
787
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18287
85.0%
Common 3233
 
15.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3453
18.9%
n 3354
18.3%
a 1776
9.7%
r 1760
9.6%
g 888
 
4.9%
E 879
 
4.8%
d 807
 
4.4%
c 794
 
4.3%
i 794
 
4.3%
x 794
 
4.3%
Other values (11) 2988
16.3%
Common
ValueCountFrequency (%)
- 872
27.0%
) 787
24.3%
( 787
24.3%
787
24.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21520
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3453
16.0%
n 3354
15.6%
a 1776
 
8.3%
r 1760
 
8.2%
g 888
 
4.1%
E 879
 
4.1%
- 872
 
4.1%
d 807
 
3.8%
c 794
 
3.7%
i 794
 
3.7%
Other values (15) 6143
28.5%
Distinct311
Distinct (%)31.1%
Missing0
Missing (%)0.0%
Memory size60.7 KiB
2023-12-09T20:31:24.153343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.07
Min length1

Characters and Unicode

Total characters5070
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique155 ?
Unique (%)15.5%

Sample

1st row59116
2nd row82506
3rd row64495
4th row35895
5th row87792
ValueCountFrequency (%)
59116 52
 
5.2%
82506 26
 
2.6%
62370 25
 
2.5%
58700 22
 
2.2%
75000 22
 
2.2%
92301 20
 
2.0%
15.5 19
 
1.9%
58682 19
 
1.9%
64922 16
 
1.6%
41887 16
 
1.6%
Other values (301) 763
76.3%
2023-12-09T20:31:24.634465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 918
18.1%
5 607
12.0%
1 576
11.4%
7 525
10.4%
6 477
9.4%
8 421
8.3%
2 405
8.0%
4 384
7.6%
3 353
 
7.0%
9 330
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4996
98.5%
Other Punctuation 74
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 918
18.4%
5 607
12.1%
1 576
11.5%
7 525
10.5%
6 477
9.5%
8 421
8.4%
2 405
8.1%
4 384
7.7%
3 353
 
7.1%
9 330
 
6.6%
Other Punctuation
ValueCountFrequency (%)
. 74
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5070
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 918
18.1%
5 607
12.0%
1 576
11.4%
7 525
10.4%
6 477
9.4%
8 421
8.3%
2 405
8.0%
4 384
7.6%
3 353
 
7.0%
9 330
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5070
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 918
18.1%
5 607
12.0%
1 576
11.4%
7 525
10.4%
6 477
9.4%
8 421
8.3%
2 405
8.0%
4 384
7.6%
3 353
 
7.0%
9 330
 
6.5%
Distinct418
Distinct (%)41.8%
Missing0
Missing (%)0.0%
Memory size61.0 KiB
2023-12-09T20:31:25.023049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.303
Min length2

Characters and Unicode

Total characters5303
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique238 ?
Unique (%)23.8%

Sample

1st row72000
2nd row94363
3rd row96790
4th row53478
5th row87792
ValueCountFrequency (%)
91768 20
 
2.0%
94882 16
 
1.6%
130000 15
 
1.5%
93587 14
 
1.4%
69709 12
 
1.2%
19.9 12
 
1.2%
71726 12
 
1.2%
208826 11
 
1.1%
67983 11
 
1.1%
75000 11
 
1.1%
Other values (408) 866
86.6%
2023-12-09T20:31:25.820651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1162
21.9%
1 712
13.4%
7 536
10.1%
8 474
8.9%
6 441
 
8.3%
9 418
 
7.9%
5 418
 
7.9%
4 370
 
7.0%
3 361
 
6.8%
2 340
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5232
98.7%
Other Punctuation 71
 
1.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1162
22.2%
1 712
13.6%
7 536
10.2%
8 474
9.1%
6 441
 
8.4%
9 418
 
8.0%
5 418
 
8.0%
4 370
 
7.1%
3 361
 
6.9%
2 340
 
6.5%
Other Punctuation
ValueCountFrequency (%)
. 71
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1162
21.9%
1 712
13.4%
7 536
10.1%
8 474
8.9%
6 441
 
8.3%
9 418
 
7.9%
5 418
 
7.9%
4 370
 
7.0%
3 361
 
6.8%
2 340
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1162
21.9%
1 712
13.4%
7 536
10.1%
8 474
8.9%
6 441
 
8.3%
9 418
 
7.9%
5 418
 
7.9%
4 370
 
7.0%
3 361
 
6.8%
2 340
 
6.4%
Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size61.6 KiB
2023-12-09T20:31:25.983822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.995
Min length5

Characters and Unicode

Total characters5995
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAnnual
2nd rowAnnual
3rd rowAnnual
4th rowAnnual
5th rowAnnual
ValueCountFrequency (%)
annual 921
92.1%
hourly 74
 
7.4%
daily 5
 
0.5%
2023-12-09T20:31:26.245619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 1842
30.7%
l 1000
16.7%
u 995
16.6%
a 926
15.4%
A 921
15.4%
y 79
 
1.3%
H 74
 
1.2%
o 74
 
1.2%
r 74
 
1.2%
D 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4995
83.3%
Uppercase Letter 1000
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1842
36.9%
l 1000
20.0%
u 995
19.9%
a 926
18.5%
y 79
 
1.6%
o 74
 
1.5%
r 74
 
1.5%
i 5
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
A 921
92.1%
H 74
 
7.4%
D 5
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 5995
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1842
30.7%
l 1000
16.7%
u 995
16.6%
a 926
15.4%
A 921
15.4%
y 79
 
1.3%
H 74
 
1.2%
o 74
 
1.2%
r 74
 
1.2%
D 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5995
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1842
30.7%
l 1000
16.7%
u 995
16.6%
a 926
15.4%
A 921
15.4%
y 79
 
1.3%
H 74
 
1.2%
o 74
 
1.2%
r 74
 
1.2%
D 5
 
0.1%
Distinct180
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size77.2 KiB
2023-12-09T20:31:26.666461image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length30
Median length27
Mean length21.963
Min length6

Characters and Unicode

Total characters21963
Distinct characters67
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)8.5%

Sample

1st row33 Beaver St, New York Ny
2nd row42-09 28th Street
3rd row109 E 16Th St., N.Y.
4th row5 Manhattan West
5th rowWards Island, N.Y.
ValueCountFrequency (%)
ny 277
 
6.5%
street 225
 
5.3%
st 222
 
5.2%
ave 164
 
3.9%
n.y 159
 
3.7%
30-30 99
 
2.3%
42-09 95
 
2.2%
28th 95
 
2.2%
i 81
 
1.9%
thomson 80
 
1.9%
Other values (385) 2759
64.8%
2023-12-09T20:31:27.235515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3256
 
14.8%
e 1490
 
6.8%
t 1486
 
6.8%
r 1124
 
5.1%
o 857
 
3.9%
a 854
 
3.9%
n 844
 
3.8%
0 617
 
2.8%
N 553
 
2.5%
S 528
 
2.4%
Other values (57) 10354
47.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10773
49.1%
Uppercase Letter 3537
 
16.1%
Space Separator 3256
 
14.8%
Decimal Number 3145
 
14.3%
Other Punctuation 877
 
4.0%
Dash Punctuation 371
 
1.7%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1490
13.8%
t 1486
13.8%
r 1124
10.4%
o 857
 
8.0%
a 854
 
7.9%
n 844
 
7.8%
y 512
 
4.8%
h 463
 
4.3%
l 431
 
4.0%
i 412
 
3.8%
Other values (15) 2300
21.3%
Uppercase Letter
ValueCountFrequency (%)
N 553
15.6%
S 528
14.9%
C 325
9.2%
Y 271
 
7.7%
A 236
 
6.7%
B 231
 
6.5%
T 186
 
5.3%
W 184
 
5.2%
E 168
 
4.7%
H 152
 
4.3%
Other values (14) 703
19.9%
Decimal Number
ValueCountFrequency (%)
0 617
19.6%
5 467
14.8%
2 411
13.1%
1 398
12.7%
3 323
10.3%
9 274
8.7%
4 233
 
7.4%
6 162
 
5.2%
8 147
 
4.7%
7 113
 
3.6%
Other Punctuation
ValueCountFrequency (%)
. 519
59.2%
, 342
39.0%
& 15
 
1.7%
/ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
3256
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 371
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14310
65.2%
Common 7653
34.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1490
 
10.4%
t 1486
 
10.4%
r 1124
 
7.9%
o 857
 
6.0%
a 854
 
6.0%
n 844
 
5.9%
N 553
 
3.9%
S 528
 
3.7%
y 512
 
3.6%
h 463
 
3.2%
Other values (39) 5599
39.1%
Common
ValueCountFrequency (%)
3256
42.5%
0 617
 
8.1%
. 519
 
6.8%
5 467
 
6.1%
2 411
 
5.4%
1 398
 
5.2%
- 371
 
4.8%
, 342
 
4.5%
3 323
 
4.2%
9 274
 
3.6%
Other values (8) 675
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21963
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3256
 
14.8%
e 1490
 
6.8%
t 1486
 
6.8%
r 1124
 
5.1%
o 857
 
3.9%
a 854
 
3.9%
n 844
 
3.8%
0 617
 
2.8%
N 553
 
2.5%
S 528
 
2.4%
Other values (57) 10354
47.1%
Distinct477
Distinct (%)47.7%
Missing0
Missing (%)0.0%
Memory size76.9 KiB
2023-12-09T20:31:27.572122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length38
Median length24
Mean length21.485
Min length3

Characters and Unicode

Total characters21485
Distinct characters76
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique285 ?
Unique (%)28.5%

Sample

1st rowBudget, Accounting & Purchasng
2nd rowHIV Programs
3rd rowCustomized Assist Svc-NM
4th rowCitywide User Support
5th rowMarine Section
ValueCountFrequency (%)
164
 
5.7%
of 89
 
3.1%
office 66
 
2.3%
services 56
 
2.0%
management 47
 
1.6%
support 43
 
1.5%
information 31
 
1.1%
staff 27
 
0.9%
mgmt 27
 
0.9%
dept 24
 
0.8%
Other values (712) 2279
79.9%
2023-12-09T20:31:28.063507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1848
 
8.6%
e 1438
 
6.7%
i 1180
 
5.5%
n 1132
 
5.3%
t 1071
 
5.0%
r 937
 
4.4%
a 922
 
4.3%
o 864
 
4.0%
s 727
 
3.4%
S 661
 
3.1%
Other values (66) 10705
49.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12229
56.9%
Uppercase Letter 6593
30.7%
Space Separator 1859
 
8.7%
Other Punctuation 499
 
2.3%
Dash Punctuation 207
 
1.0%
Decimal Number 50
 
0.2%
Open Punctuation 17
 
0.1%
Close Punctuation 17
 
0.1%
Math Symbol 11
 
0.1%
Control 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1438
11.8%
i 1180
9.6%
n 1132
 
9.3%
t 1071
 
8.8%
r 937
 
7.7%
a 922
 
7.5%
o 864
 
7.1%
s 727
 
5.9%
c 543
 
4.4%
l 474
 
3.9%
Other values (17) 2941
24.0%
Uppercase Letter
ValueCountFrequency (%)
S 661
 
10.0%
E 531
 
8.1%
A 512
 
7.8%
N 479
 
7.3%
I 450
 
6.8%
M 425
 
6.4%
C 415
 
6.3%
T 410
 
6.2%
O 393
 
6.0%
P 378
 
5.7%
Other values (17) 1939
29.4%
Other Punctuation
ValueCountFrequency (%)
/ 261
52.3%
& 98
 
19.6%
. 96
 
19.2%
, 20
 
4.0%
' 18
 
3.6%
: 4
 
0.8%
# 2
 
0.4%
Decimal Number
ValueCountFrequency (%)
1 21
42.0%
3 11
22.0%
2 10
20.0%
7 5
 
10.0%
5 2
 
4.0%
4 1
 
2.0%
Space Separator
ValueCountFrequency (%)
1848
99.4%
  11
 
0.6%
Control
ValueCountFrequency (%)
€ 1
50.0%
“ 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 207
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Math Symbol
ValueCountFrequency (%)
+ 11
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18822
87.6%
Common 2663
 
12.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1438
 
7.6%
i 1180
 
6.3%
n 1132
 
6.0%
t 1071
 
5.7%
r 937
 
5.0%
a 922
 
4.9%
o 864
 
4.6%
s 727
 
3.9%
S 661
 
3.5%
c 543
 
2.9%
Other values (44) 9347
49.7%
Common
ValueCountFrequency (%)
1848
69.4%
/ 261
 
9.8%
- 207
 
7.8%
& 98
 
3.7%
. 96
 
3.6%
1 21
 
0.8%
, 20
 
0.8%
' 18
 
0.7%
( 17
 
0.6%
) 17
 
0.6%
Other values (12) 60
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21460
99.9%
None 25
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1848
 
8.6%
e 1438
 
6.7%
i 1180
 
5.5%
n 1132
 
5.3%
t 1071
 
5.0%
r 937
 
4.4%
a 922
 
4.3%
o 864
 
4.0%
s 727
 
3.4%
S 661
 
3.1%
Other values (61) 10680
49.8%
None
ValueCountFrequency (%)
  11
44.0%
 11
44.0%
â 1
 
4.0%
€ 1
 
4.0%
“ 1
 
4.0%
Distinct900
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
2023-12-09T20:31:28.462793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length11107
Median length3705
Mean length3365.989
Min length256

Characters and Unicode

Total characters3365989
Distinct characters108
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique809 ?
Unique (%)80.9%

Sample

1st rowThe New York City Taxi and Limousine Commission (TLC) is the nation’s largest for-hire transportation agency, licensing and regulating the City’s yellow and green taxicabs, for-hire vehicles, rideshare companies, commuter vans, and luxury limousines. TLC develops and enforces rules to promote safety, transparency, and accessibility, and works to promote consumer and driver protection for a vital mode of transport in New York City. TLC-regulated drivers, vehicles, and businesses move over one million people a day. With the introduction of new apps and technologies, TLC is on the front lines of a changing mobility landscape. To learn more about the TLC, please visit: www.nyc.gov/taxi TLC is seeking a Program Coordinator for the Office of Financial Stability (OFS) to support the economic health of the Yellow Medallion Taxicab, a cultural icon of New York City that provides jobs for thousands of hardworking New Yorkers. OFS reports to the Assistant Commissioner of Program Planning and Management within the Finance Division at the TLC. The Program Coordinator will play a pivotal role in evaluating the impact of the Medallion Relief Program (MRP), which has provided $430 million in debt relief to small business taxi owners and drivers. In addition, the Program Coordinator will study key taxi indicators, support the annual financial disclosure project, and develop programming initiatives. OFS also works closely with the Owner/Driver Resource Center to ensure small business taxi operators have the resources they need to be successful. This is a position for someone who is interested in supporting small businesses and working at the intersection of data, policy, and programs. Responsibilities include: • Monitor and evaluate the Medallion Relief Program. Study impact of grants on small business medallion owners using qualitative and quantitative data. • Work collaboratively with team to gather and study taxi economic health metrics. Develop programming in partnership with the Owner/Driver Resource Center to support small business taxi owners. • Manage medallion financial disclosure workstreams. Coordinate with internal and external partners to develop and implement new business practice standards. • Support senior leadership in producing and publishing annual reports, slide decks, policy memos, workflows, and other materials as needed. Analyze and present findings to internal and external stakeholders. • Represent the Agency in interactions with TLC customers, industry stakeholders, advocacy groups, City, State, and Federal Agencies and Offices, vendors, and members of the public. Develop productive relationships with Agency partners. • Coordinate special projects as assigned.
2nd rowEstablished in 1805, the New York City Department of Health and Mental Hygiene (the NYC Health Department) is the oldest and largest health department in the country. Our mission is to protect and improve the health of all New Yorkers, in service of a vision of a city in which all New Yorkers can realize their full health potential, regardless of who they are, how old they are, where they are from, or where they live. As a world-renowned public health agency with a history of building transformative public health programming and infrastructure, innovating in science and scholarship to advance public health knowledge, and responding to urgent public health crises — from New York City’s yellow fever outbreak in 1822, to the COVID-19 pandemic — we are a hub for public health innovation, expertise, and programs, and services. We serve as the population health strategist, and policy, and planning authority for the City of New York, while also having a vast impact on national and international public policy, including programs and services focused on food and nutrition, anti-tobacco support, chronic disease prevention, HIV/AIDS treatment, family and child health, environmental health, mental health, and racial and social justice work, among others. Our Agency’s five strategic priorities, building off a recently-completed strategic planning process emerging from the COVID-19 emergency, are: 1) To re-envision how the Health Department prepares for and responds to health emergencies, with a focus on building a “response-ready” organization, with faster decision-making, transparent public communications, and stronger surveillance and bridges to healthcare systems 2) Address and prevent chronic and diet-related disease, including addressing rising rates of childhood obesity and the impact of diabetes, and transforming our food systems to improve nutrition and enhance access to healthy foods 3) Address the second pandemic of mental illness including: reducing overdose deaths, strengthening our youth mental health systems, and supporting people with serious mental illness 4) Reduce black maternal mortality and make New York a model city for women’s health 5) Mobilize against and combat the health impacts of climate change Our 7,000-plus team members bring extraordinary diversity to the work of public health. True to our value of equity as a foundational element of all of our work, and a critical foundation to achieving population health impact in New York City, the NYC Health Department has been a leader in recognizing and dismantling racism’s impacts on the health of New Yorkers and beyond. In 2021, the NYC Board of Health declared racism as a public health crisis. With commitment to advance anti-racist public health practices that dismantle systems that perpetuate inequitable power, opportunity and access, the NYC Health Department continues to work in and with communities and community organizations to increase their access to health services and decrease avoidable health outcomes. PROGRAM AND JOB DESCRIPTION: The New York City Department of Health and Mental Hygiene (NYC DOHMH)’s Bureau of Hepatitis, HIV, and Sexually Transmitted Infections (BHHS) oversees the City’s response to viral hepatitis, HIV, and sexually transmitted infections (STIs). Its mission is to improve the lives of New Yorkers by ending transmission, illness, stigma, and inequities related to viral hepatitis, HIV, and STIs. BHHS’s work includes testing initiatives; prevention, care, and treatment programming; epidemiology and surveillance; training and technical assistance; community engagement; social marketing; policy advocacy; and racial equity and social justice initiatives. The Clinical Operations and Technical Assistance (COTA) program works to measure, report on, and improve the quality of prevention and care services delivered to people with HIV (PWH) as well as those vulnerable to contracting HIV. The COTA program engages in a variety of activities including clinical and non-clinical provider trainings, assessment of clinics via site visits and survey assessments, provision of targeted technical assistance (TA), production of resources and presentations on findings and implications, and engagement with internal and external partners. As part of the national plan to End the HIV epidemic (EHE), COTA’s HIV Clinical TA team develops and delivers clinical technical assistance (TA) to NYC-based HIV clinics to strengthen the capacity of the HIV workforce and improve clinical outcomes among PWH in NYC.HIV clinical TA services will involve conducting virtual and in-person visits with clinicians and social service providers across NYC to provide tailored TA on topics such as immediate initiation of antiretroviral therapy (iART), long-acting ART, and any other HIV biomedical interventions. The TA materials will consist of resources that promote provider learning, clinical implementation and scale up, and client-centered shared decision-making tools. In addition, the HIV Clinical TA team will incorporate Racial Equity and Social Justice principles to ensure our services meet the needs of priority populations (i.e., men who have sex with men of color, women of color, transgender women, youth [ages 13-24], and older PWH). COTA seeks one HIV Clinical Technical Assistance (TA) Specialist to establish strong relationships with clinical and non-clinical NYC based HIV providers and key stakeholders to deliver data-informed technical assistance.  DUTIES WILL INCLUDE BUT NOT BE LIMITED TO: Support the development and overall design of the teams' current long-acting ART TA strategy, utilizing a data-informed, and racial equity and social justice approach to increase awareness and uptake of long-acting ART among HIV clinical providers and PWH. Develop, pilot and produce technical resource materials for HIV clinics and PWH. Deliver on-going TA and capacity building assistance to visited agencies, which may entail developing curricula, conducting trainings, developing and/or delivering webinars, facilitating learning communities, making linkages between members of different organizations (peer-to-peer), and conducting routine site visits. Collaborate with COTA's Planning, Monitoring, and Evaluation (PME) team to develop evaluation plans for HIV Clinical TA projects and activities. Work with Salesforce leads to facilitate the reporting of HIV Clinical TA activities and perform data quality checks on all data entered into Salesforce. Develop and apply systematic reviews of recently published literature and clinical guidelines to inform the development of TA resources and presentations. Partner with BHHS Programs to understand current BHHS activities to inform the development of TA activities. Contribute to annual and semi-annual reports to funders. Identify and develop opportunities for making contributions to the public health literature by submitting abstracts to national conferences and papers to peer review journals. Represent and present on current and future HIV bio-medical therapeutics as requested at both internal and external meetings. Perform ad-hoc duties as may be assigned by supervisor.
3rd rowCustomized Assistance Services (CAS) helps Human Resources Administration (HRA) clients with health and/or mental health conditions reach their highest attainable level of functioning and self-sufficiency by providing comprehensive, integrated, individualized clinical and supportive services. CAS works with other components of HRA and with other governmental and non-governmental service provides to create new programs and to integrate and refine existing services so the people it serves can achieve their maximum functional capacity. Under the direction of the Director, the Administrative Assistant to the Director (AA to Director)/Associate Job Opportunity Specialist II (AJOS II) is responsible for supervising a team of AJOS staff, who provide various functions of eligibility determination, financial planning, and employment planning and monitoring and other related services for clients requesting and in receipt of Homebound services from HRA. The AA to the Director/AJOS II uses supervisory, program development, quantitative analysis, and other research skills in accomplishing all the goals of CAS Benefits Access Center and its components (Application, Financial Planning, Employment Planning, Undercare, et.) The Customized Assistance Services is recruiting for (1) Associate Job Opportunity Specialist II (AJOS II) who will: • Monitor the workflow of AJOS workers who handle the entire application process for all new applicants and ongoing cases, including face-to-face recertifications; Family Assistance cases which have been closed for over sixty days; all Safety Net case reopens which have been closed regardless of date; and one-shot deals. • Review specific cases and authorize benefits as required. • Review the accuracy and timeliness of reports, including all relevant worklists. • Act as liaison to the Rental Assistance Unit. • Conduct daily sweeps to ensure timely and proper service. • Manage a team of AJOS workers who interview applicants and assess eligibility for immediate needs grants (food and non-food), employability and eligibility for public assistance, food stamps and Medicaid; works with applicants to remove barriers to employment and makes referrals to other services as needed. • Manage a team of AJOS workers who provide comprehensive service delivery to participants after the establishment of the participant’s case; Family Assistance cases that have been closed less than sixty days; or Safety Net cases that have been closed in error. • Supervise a team of AJOS workers who manage all aspects of the cash assistance case, including establishing on- going eligibility, assessing participants, developing appropriate Employment Assessment and Employment Plans and executing specific strategies designed to help participants achieve self- sufficiency. • Ensure adequate planning, scheduling and monitoring of case management activities and conduct regular reviews of outcome reports, worklists, audits and assess client feedback to ensure the team is maintaining adequate levels of performance necessary to move participants toward self-sufficiency. • Assist with case consultation on individual cases and guidance on difficult cases in the case planning process as necessary to ensure the appropriate approach is developed to suit the participant’s needs and achieve the best plan for the individual to progress to self-sufficiency. • Oversee teams of clerical and JOS workers who provide quick service for those clients who walk- in or telephone the Center. Additionally, the team will take appropriate action on reported changes, provide information and documentation as requested by the participants and maintain contact with other teams to ensure awareness of all activity that will affect the case management plan. • Oversee staff that interview and determine housing needs of tenants at risk for homelessness or already homeless reporting to Benefits Access Centers. Develop ant-eviction/housing plan of intervention for referred participants. • Monitor conference activities and prepare regular reports on unit activities. • Perform final review of packets prior to fair hearing and determines whether a resolution will be required prior to the hearing. Using Strategies and techniques aimed at securing success in the hearing room, ardently represents the Agency. • Prepare reports on key performance outcomes and ensure that necessary corrective actions are implemented in a timely manner. • May conduct field visits.
4th rowThe NYC Financial Information Services Agency-Office of Payroll Administration (FISA-OPA) is recruiting two Clerical Associates Level 2 or comparable title for Level 1 Help Desk Representatives for the Citywide User Support Division. Under general direction, the Help Desk Level 1 Representatives will serve as the “front-line” of the FISA-OPA agency’s Help Desk operation, providing caller assistance and problem management services to City agencies regarding City systems under both FISA-OPA. These systems include CityTime, the Financial Management System (FMS), the Payee Information Portal (PIP), the Payroll Management System (PMS), City Human Resource Management System (CHRMS) Remedy, Finesse – IVR and others. The Help Desk Level 1 Representatives will: • Respond to telephone (Finesse) and email inquiries and determine the information required with professionalism and tact; • Answer routine and frequently asked questions regarding user and system issues; • Escalate moderate and complex user and system issues when required; • Apprise users of the status and progress of pending requests; • Provide efficient and effective problem resolution for callers; • Initiate system password resets for authorized users; • Record and track information from initial call to resolution using the Remedy IT Service Management System; • Assist in collecting data to be used in researching user issues that may indicate larger system problems; • Prepare statistical reports for management review; • Provide system documentation and forms as required; • Run simple system procedures; • Participate in unit/staff meetings by attending /facilitating agenda / encouraging relevant discussion, in order to keep staff informed of agency/ unit goals and changes in programs/ policies/ procedures and; • Perform special projects as assigned.
5th rowThe NYC Department of Environmental Protection (DEP) enriches the environment and protects public health for all New Yorkers by providing 1.1 billion gallons of high quality drinking water, managing wastewater and stormwater, and reducing air, noise, and hazardous materials pollution. DEP is the largest combined municipal water and wastewater utility in the country, with nearly 6,000 employees. DEP's water supply system is comprised of 19 reservoirs and 3 controlled lakes throughout the system’s 2,000 square mile watershed that extends 125 miles north and west of the City. The Bureau of Wastewater Treatment (BWT) is responsible for the operation and maintenance of all facilities related to the treatment of sewage within the five boroughs of the City. This includes 14 wastewater treatment plants, sludge dewatering facilities, collections facilities (pumping stations, combined sewer overflow retention facilities, regulators, tide gates, etc.), wastewater laboratories and harbor vessels. The Chief Marine Engineer (Diesel) is responsible for supervising and directing the operation and maintenance of the Bureau's marine vessels. Duties include supervising and directing technical staff in the Marine Section; maintaining engine room logs and records; observing all federal and departmental regulations pertaining to the operation of mechanical and/or engine equipment and the marine vessels; supervising and directing the care, storage and use of fuel onboard the ship and carrying out responsibilities associated with the Marine Operations and Maintenance Section's Safety Program.
ValueCountFrequency (%)
and 32226
 
6.7%
the 22229
 
4.6%
of 15928
 
3.3%
to 14997
 
3.1%
in 7185
 
1.5%
for 6627
 
1.4%
health 6149
 
1.3%
a 6024
 
1.3%
with 5736
 
1.2%
• 4853
 
1.0%
Other values (12238) 358357
74.6%
2023-12-09T20:31:29.048707image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
496981
14.8%
e 309209
 
9.2%
t 227396
 
6.8%
i 225818
 
6.7%
n 214734
 
6.4%
a 212462
 
6.3%
o 189812
 
5.6%
r 176784
 
5.3%
s 174112
 
5.2%
l 108734
 
3.2%
Other values (98) 1029947
30.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2601750
77.3%
Space Separator 497067
 
14.8%
Uppercase Letter 152049
 
4.5%
Other Punctuation 62256
 
1.8%
Control 17022
 
0.5%
Decimal Number 15303
 
0.5%
Dash Punctuation 6449
 
0.2%
Currency Symbol 5260
 
0.2%
Close Punctuation 4856
 
0.1%
Open Punctuation 3870
 
0.1%
Other values (4) 107
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 14989
 
9.9%
A 11958
 
7.9%
S 11005
 
7.2%
T 10966
 
7.2%
D 10482
 
6.9%
P 9862
 
6.5%
E 9845
 
6.5%
N 9268
 
6.1%
I 8663
 
5.7%
O 8198
 
5.4%
Other values (18) 46813
30.8%
Lowercase Letter
ValueCountFrequency (%)
e 309209
11.9%
t 227396
 
8.7%
i 225818
 
8.7%
n 214734
 
8.3%
a 212462
 
8.2%
o 189812
 
7.3%
r 176784
 
6.8%
s 174112
 
6.7%
l 108734
 
4.2%
c 103120
 
4.0%
Other values (17) 659569
25.4%
Other Punctuation
ValueCountFrequency (%)
, 31459
50.5%
. 19464
31.3%
: 2946
 
4.7%
/ 2886
 
4.6%
; 2583
 
4.1%
' 964
 
1.5%
* 784
 
1.3%
& 612
 
1.0%
# 317
 
0.5%
¿ 78
 
0.1%
Other values (6) 163
 
0.3%
Decimal Number
ValueCountFrequency (%)
0 3755
24.5%
1 3146
20.6%
2 2146
14.0%
5 1544
10.1%
3 1069
 
7.0%
9 886
 
5.8%
8 786
 
5.1%
4 756
 
4.9%
6 671
 
4.4%
7 544
 
3.6%
Control
ValueCountFrequency (%)
€ 9036
53.1%
3916
23.0%
™ 2954
 
17.4%
” 361
 
2.1%
 352
 
2.1%
œ 288
 
1.7%
“ 102
 
0.6%
‚ 11
 
0.1%
˜ 2
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 4844
99.8%
] 8
 
0.2%
} 4
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 3858
99.7%
[ 8
 
0.2%
{ 4
 
0.1%
Math Symbol
ValueCountFrequency (%)
+ 50
80.6%
¬ 11
 
17.7%
> 1
 
1.6%
Space Separator
ValueCountFrequency (%)
496981
> 99.9%
  86
 
< 0.1%
Currency Symbol
ValueCountFrequency (%)
¢ 4999
95.0%
$ 261
 
5.0%
Other Symbol
ValueCountFrequency (%)
° 2
66.7%
© 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 6449
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 41
100.0%
Format
ValueCountFrequency (%)
­ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2753799
81.8%
Common 612190
 
18.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 309209
 
11.2%
t 227396
 
8.3%
i 225818
 
8.2%
n 214734
 
7.8%
a 212462
 
7.7%
o 189812
 
6.9%
r 176784
 
6.4%
s 174112
 
6.3%
l 108734
 
3.9%
c 103120
 
3.7%
Other values (45) 811618
29.5%
Common
ValueCountFrequency (%)
496981
81.2%
, 31459
 
5.1%
. 19464
 
3.2%
€ 9036
 
1.5%
- 6449
 
1.1%
¢ 4999
 
0.8%
) 4844
 
0.8%
3916
 
0.6%
( 3858
 
0.6%
0 3755
 
0.6%
Other values (43) 27429
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3338246
99.2%
None 27743
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
496981
14.9%
e 309209
 
9.3%
t 227396
 
6.8%
i 225818
 
6.8%
n 214734
 
6.4%
a 212462
 
6.4%
o 189812
 
5.7%
r 176784
 
5.3%
s 174112
 
5.2%
l 108734
 
3.3%
Other values (78) 1002204
30.0%
None
ValueCountFrequency (%)
â 9047
32.6%
€ 9036
32.6%
¢ 4999
18.0%
™ 2954
 
10.6%
” 361
 
1.3%
 352
 
1.3%
 289
 
1.0%
œ 288
 
1.0%
“ 102
 
0.4%
  86
 
0.3%
Other values (10) 229
 
0.8%
Distinct251
Distinct (%)25.2%
Missing4
Missing (%)0.4%
Memory size1.1 MiB
2023-12-09T20:31:29.486907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length4811
Median length2139
Mean length1090.344378
Min length51

Characters and Unicode

Total characters1085983
Distinct characters87
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique118 ?
Unique (%)11.8%

Sample

1st row1. A baccalaureate degree from an accredited college and two years of experience in community work or community centered activities in an area related to the duties described above; or 2. High school graduation or equivalent and six years of experience in community work or community centered activities in an area related to the duties as described above; or 3. Education and/or experience which is equivalent to 1 or 2 above. However, all candidates must have at least one year of experience as described in 1 above.
2nd row1. For Assignment Level I (only physical, biological and environmental sciences and public health) A master's degree from an accredited college or university with a specialization in an appropriate field of physical, biological or environmental science or in public health. To be appointed to Assignment Level II and above, candidates must have: 1. A doctorate degree from an accredited college or university with specialization in an appropriate field of physical, biological, environmental or social science and one year of full-time experience in a responsible supervisory, administrative or research capacity in the appropriate field of specialization; or 2. A master's degree from an accredited college or university with specialization in an appropriate field of physical, biological, environmental or social science and three years of responsible full-time research experience in the appropriate field of specialization; or 3. Education and/or experience which is equivalent to 1 or 2 above. However, all candidates must have at least a master's degree in an appropriate field of specialization and at least two years of experience described in 2 above. Two years as a City Research Scientist Level I can be substituted for the experience required in 1 and 2 above. NOTE: Probationary Period Appointments to this position are subject to a minimum probationary period of one year.
3rd row1. A four-year high school diploma or its educational equivalent, and three years of full-time satisfactory experience working directly in social/human services or a related setting, providing either a. client services; or b. employment planning or counseling services which involve job development, skills assessment, and employment placement or other economic opportunity programming; or 2. A baccalaureate degree from an accredited college plus eighteen months of full-time satisfactory experience working as a Job Opportunity Specialist/Benefits Opportunity Specialist; or 3. A baccalaureate degree from an accredited college; plus, eighteen months of full-time satisfactory experience as described in one (1) above; or 4. College credit from an accredited college may be substituted for this experience on the basis of 60 semester credits for 9 months of the work experience described above. However, all candidates must have 18 months of full-time satisfactory experience working as a Job Opportunity Specialist/Benefits Opportunity Specialist or performing social/human services work as described in one (1) above.
4th rowQualification Requirements A four-year high school diploma or its educational equivalent approved by a State's department of education or a recognized accrediting organization and one year of satisfactory clerical experience. Skills Requirement Keyboard familiarity with the ability to type at a minimum of 100 key strokes (20 words) per minute.
5th rowQualification Requirements For appointment in the Department of Public Works, a valid license for Chief Engineer of Motor vessels, not less than 3,000 H.P., issued by the United States Coast Guard Inspection Service. 1. Five (5) years of full-time, satisfactory paid experience acquired in the last 15 years as a Marine Engineer (Diesel).
ValueCountFrequency (%)
or 7939
 
5.0%
of 7195
 
4.5%
in 6007
 
3.8%
a 5447
 
3.4%
experience 4423
 
2.8%
the 4180
 
2.6%
and 3279
 
2.0%
an 2912
 
1.8%
to 2873
 
1.8%
above 2544
 
1.6%
Other values (1874) 113172
70.7%
2023-12-09T20:31:30.106318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
165627
15.3%
e 119035
 
11.0%
i 77666
 
7.2%
a 75443
 
6.9%
o 66195
 
6.1%
r 63702
 
5.9%
n 63328
 
5.8%
t 62806
 
5.8%
s 47012
 
4.3%
c 43753
 
4.0%
Other values (77) 301416
27.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 864339
79.6%
Space Separator 165627
 
15.3%
Other Punctuation 21602
 
2.0%
Uppercase Letter 18802
 
1.7%
Decimal Number 8363
 
0.8%
Control 2271
 
0.2%
Dash Punctuation 2182
 
0.2%
Close Punctuation 1372
 
0.1%
Open Punctuation 1327
 
0.1%
Currency Symbol 50
 
< 0.1%
Other values (2) 48
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 119035
13.8%
i 77666
 
9.0%
a 75443
 
8.7%
o 66195
 
7.7%
r 63702
 
7.4%
n 63328
 
7.3%
t 62806
 
7.3%
s 47012
 
5.4%
c 43753
 
5.1%
l 35016
 
4.1%
Other values (17) 210383
24.3%
Uppercase Letter
ValueCountFrequency (%)
A 3800
20.2%
I 2215
11.8%
S 1642
8.7%
E 1536
8.2%
L 1304
 
6.9%
C 1033
 
5.5%
H 975
 
5.2%
T 951
 
5.1%
N 871
 
4.6%
R 792
 
4.2%
Other values (16) 3683
19.6%
Decimal Number
ValueCountFrequency (%)
1 3137
37.5%
2 2077
24.8%
3 1111
 
13.3%
0 810
 
9.7%
6 389
 
4.7%
4 380
 
4.5%
8 219
 
2.6%
5 156
 
1.9%
9 63
 
0.8%
7 21
 
0.3%
Other Punctuation
ValueCountFrequency (%)
, 10580
49.0%
. 6473
30.0%
; 2019
 
9.3%
/ 1349
 
6.2%
: 639
 
3.0%
' 524
 
2.4%
? 16
 
0.1%
* 2
 
< 0.1%
Control
ValueCountFrequency (%)
€ 1106
48.7%
œ 484
21.3%
 413
 
18.2%
™ 179
 
7.9%
62
 
2.7%
“ 24
 
1.1%
˜ 2
 
0.1%
 1
 
< 0.1%
Currency Symbol
ValueCountFrequency (%)
$ 46
92.0%
¢ 4
 
8.0%
Space Separator
ValueCountFrequency (%)
165627
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2182
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1372
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1327
100.0%
Other Number
ValueCountFrequency (%)
½ 45
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 883141
81.3%
Common 202842
 
18.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 119035
13.5%
i 77666
 
8.8%
a 75443
 
8.5%
o 66195
 
7.5%
r 63702
 
7.2%
n 63328
 
7.2%
t 62806
 
7.1%
s 47012
 
5.3%
c 43753
 
5.0%
l 35016
 
4.0%
Other values (43) 229185
26.0%
Common
ValueCountFrequency (%)
165627
81.7%
, 10580
 
5.2%
. 6473
 
3.2%
1 3137
 
1.5%
- 2182
 
1.1%
2 2077
 
1.0%
; 2019
 
1.0%
) 1372
 
0.7%
/ 1349
 
0.7%
( 1327
 
0.7%
Other values (24) 6699
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1082575
99.7%
None 3408
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
165627
15.3%
e 119035
 
11.0%
i 77666
 
7.2%
a 75443
 
7.0%
o 66195
 
6.1%
r 63702
 
5.9%
n 63328
 
5.8%
t 62806
 
5.8%
s 47012
 
4.3%
c 43753
 
4.0%
Other values (67) 298008
27.5%
None
ValueCountFrequency (%)
â 1106
32.5%
€ 1106
32.5%
œ 484
14.2%
 413
 
12.1%
™ 179
 
5.3%
½ 45
 
1.3%
 45
 
1.3%
“ 24
 
0.7%
¢ 4
 
0.1%
˜ 2
 
0.1%

preferred_skills
Text

MISSING 

Distinct688
Distinct (%)85.4%
Missing194
Missing (%)19.4%
Memory size550.3 KiB
2023-12-09T20:31:30.493532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length2977
Median length859
Mean length621.6066998
Min length29

Characters and Unicode

Total characters501015
Distinct characters94
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique588 ?
Unique (%)73.0%

Sample

1st row• Project management experience coordinating with multiple internal and external stakeholders. • Experience using quantitative and qualitative data for program evaluation. • Takes initiative and works collaboratively across teams. • Flexible and creative problem-solving skills. • Strong communicator with excellent verbal, written, and presentation skills.
2nd rowExcellent communication skills, including written, oral, and interpersonal. Ability to assess HIV clinical providers' needs, communicate clinical information, and deliver engaging presentations. Outstanding organizational, time and project management skills. Knowledgeable about HIV/AIDS prevention and treatment. A minimum of 3 years of experience in public health project management and implementation. A minimum of 1 year of experience working with clinical and non-clinical providers in a clinical, research, or other health care setting, or equivalent clinical training. Ability to collaborate with subject matter experts and managers in training and TA resource development, delivery and modification. Experience with abstract and manuscript development. Solid working knowledge of MS Office software. Keen attention to detail and ability to handle multiple and diverse assignments efficiently and effectively. Ability to work effectively with a diverse staff of trainer, planners, capacity-building experts, clinicians, program managers, and service providers within the NYC DOHMH and at external agencies, including HRSA. Exceptional relationship-building and influencing skills a plus.
3rd row• Monitor conference activities and prepare regular reports on unit activities. • Ability to multitask, work independently as well as in a team and consistently maintain a professional demeanor. • Proficient knowledge of WMS, HRA One Viewer, Microsoft Outlook, Excel, Word and Access. • Strong research and problem-solving skills. • Good time management skills.
4th row• Basic knowledge of PMS, CityTime, FMS, PIP,CHRMS or Remedy • Excellent work ethic and attention to detail • Excellent verbal and written communication skills • Excellent telephone skills with a professional demeanor • Ability to work independently or as part of a team • Ability to work well and efficient in a fast-paced environment • Ability to maintain confidentiality • Only permanent employees in the Clerical Associate title (or comparable title) and those who are reachable on the civil service list are eligible to apply
5th rowA valid license for Chief Engineer of Motor vessels, not less than 6,000 H.P., issued by the United States Coast Guard Inspection Service.
ValueCountFrequency (%)
and 4901
 
7.2%
• 2631
 
3.9%
to 2000
 
2.9%
of 1763
 
2.6%
in 1524
 
2.2%
experience 1355
 
2.0%
with 1315
 
1.9%
skills 1306
 
1.9%
a 1146
 
1.7%
the 1070
 
1.6%
Other values (4418) 49244
72.1%
2023-12-09T20:31:31.063416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
68532
13.7%
e 45474
 
9.1%
i 37036
 
7.4%
n 34566
 
6.9%
t 32076
 
6.4%
a 31266
 
6.2%
o 26799
 
5.3%
r 25302
 
5.1%
s 23366
 
4.7%
l 20913
 
4.2%
Other values (84) 155685
31.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 391263
78.1%
Space Separator 68532
 
13.7%
Uppercase Letter 18318
 
3.7%
Other Punctuation 11186
 
2.2%
Control 5258
 
1.0%
Currency Symbol 2688
 
0.5%
Decimal Number 1473
 
0.3%
Dash Punctuation 1113
 
0.2%
Close Punctuation 559
 
0.1%
Open Punctuation 523
 
0.1%
Other values (2) 102
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 2356
12.9%
A 2045
11.2%
E 1990
10.9%
P 1564
 
8.5%
C 1496
 
8.2%
M 1151
 
6.3%
D 772
 
4.2%
O 736
 
4.0%
N 634
 
3.5%
I 610
 
3.3%
Other values (18) 4964
27.1%
Lowercase Letter
ValueCountFrequency (%)
e 45474
11.6%
i 37036
 
9.5%
n 34566
 
8.8%
t 32076
 
8.2%
a 31266
 
8.0%
o 26799
 
6.8%
r 25302
 
6.5%
s 23366
 
6.0%
l 20913
 
5.3%
c 16482
 
4.2%
Other values (17) 97983
25.0%
Other Punctuation
ValueCountFrequency (%)
, 5280
47.2%
. 4314
38.6%
; 674
 
6.0%
/ 484
 
4.3%
: 176
 
1.6%
& 72
 
0.6%
' 68
 
0.6%
* 68
 
0.6%
¿ 25
 
0.2%
· 15
 
0.1%
Other values (2) 10
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 275
18.7%
3 206
14.0%
5 201
13.6%
2 187
12.7%
0 172
11.7%
6 136
9.2%
4 116
7.9%
7 83
 
5.6%
8 55
 
3.7%
9 42
 
2.9%
Control
ValueCountFrequency (%)
€ 2904
55.2%
2135
40.6%
™ 180
 
3.4%
“ 19
 
0.4%
 10
 
0.2%
œ 9
 
0.2%
‚ 1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+ 52
83.9%
> 9
 
14.5%
¬ 1
 
1.6%
Currency Symbol
ValueCountFrequency (%)
¢ 2686
99.9%
$ 2
 
0.1%
Space Separator
ValueCountFrequency (%)
68532
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1113
100.0%
Close Punctuation
ValueCountFrequency (%)
) 559
100.0%
Open Punctuation
ValueCountFrequency (%)
( 523
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 409581
81.8%
Common 91434
 
18.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 45474
 
11.1%
i 37036
 
9.0%
n 34566
 
8.4%
t 32076
 
7.8%
a 31266
 
7.6%
o 26799
 
6.5%
r 25302
 
6.2%
s 23366
 
5.7%
l 20913
 
5.1%
c 16482
 
4.0%
Other values (45) 116301
28.4%
Common
ValueCountFrequency (%)
68532
75.0%
, 5280
 
5.8%
. 4314
 
4.7%
€ 2904
 
3.2%
¢ 2686
 
2.9%
2135
 
2.3%
- 1113
 
1.2%
; 674
 
0.7%
) 559
 
0.6%
( 523
 
0.6%
Other values (29) 2714
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 492219
98.2%
None 8796
 
1.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
68532
13.9%
e 45474
 
9.2%
i 37036
 
7.5%
n 34566
 
7.0%
t 32076
 
6.5%
a 31266
 
6.4%
o 26799
 
5.4%
r 25302
 
5.1%
s 23366
 
4.7%
l 20913
 
4.2%
Other values (71) 146889
29.8%
None
ValueCountFrequency (%)
â 2904
33.0%
€ 2904
33.0%
¢ 2686
30.5%
™ 180
 
2.0%
 41
 
0.5%
¿ 25
 
0.3%
“ 19
 
0.2%
· 15
 
0.2%
 10
 
0.1%
œ 9
 
0.1%
Other values (3) 3
 
< 0.1%
Distinct375
Distinct (%)51.9%
Missing278
Missing (%)27.8%
Memory size584.6 KiB
2023-12-09T20:31:31.513709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length1910
Median length1297
Mean length751.0484765
Min length4

Characters and Unicode

Total characters542257
Distinct characters96
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique271 ?
Unique (%)37.5%

Sample

1st row**IMPORTANT NOTES TO ALL CANDIDATES: Please note: If you are called for an interview you will be required to bring to your interview copies of original documentation, such as: • A document that establishes identity for employment eligibility, such as: A Valid U.S. Passport, Permanent Resident Card/Green Card, or Driver’s license. • Proof of Education according to the education requirements of the civil service title. • Current Resume • Proof of Address/NYC Residency dated within the last 60 days, such as: Recent Utility Bill (i.e. Telephone, Cable, Mobile Phone) Additional documentation may be required to evaluate your qualification as outlined in this posting’s “Minimum Qualification Requirements” section. Examples of additional documentation may be, but not limited to: college transcript, experience verification or professional trade licenses. If after your interview you are the selected candidate you will be contacted to schedule an on-boarding appointment. By the time of this appointment you will be asked to produce the originals of the above documents along with your original Social Security card. **LOAN FORGIVENESS The federal government provides student loan forgiveness through its Public Service Loan Forgiveness Program (PSLF) to all qualifying public service employees. Working with the DOHMH qualifies you as a public service employee and you may be able to take advantage of this program while working full-time and meeting the program’s other requirements. Please visit the Public Service Loan Forgiveness Program site to view the eligibility requirements: https://studentaid.ed.gov/sa/repay-loans/forgiveness-cancellation/public-service FINAL APPOINTMENTS ARE SUBJECT TO OFFICE OF MANAGEMENT & BUDGET APPROVAL”
2nd row**LOAN FORGIVENESS The federal government provides student loan forgiveness through its Public Service Loan Forgiveness Program (PSLF) to all qualifying public service employees. Working with the DSS/HRA/DHS qualifies you as a public service employee and you may be able to take advantage of this program while working full-time and meeting the program’s other requirements. Please visit the Public Service Loan Forgiveness Program site to view the eligibility requirements: https://studentaid.ed.gov/sa/repay-loans/forgiveness-cancellation/public-service In addition, the Human Resources Administration/Department of Social Services offers competitive salaries and the following benefits: Generous Pension Plans (The New York Employees' Retirement System); 401(k) and Roth 457 Retirement Savings Programs; U.S. Savings Bonds Flexible Spending Program; Health Benefits, Dental, Vision Coverage, Prescription Drug Program; Training and Professional Development; Opportunity for Scholarship; College Savings Program; Paid Holidays and Generous Annual Leave. The City of New York is an Equal Opportunity Employer
3rd row#O-143 & O-154
4th rowAppointments are subject to OMB approval. For additional information about DEP, visit www.nyc.gov/dep. DEP is an equal opportunity employer with a strong commitment to the diversity of our organization and workforce. Your voluntary response to the NYCAPS on-line application section for referral information will assist us tremendously in our ability to track the success of our outreach and recruitment efforts. Please be sure to indicate your source of referral to this job. NOTE:This position is open to qualified persons with a disability who are eligible for the 55-a Program. Please indicate on your resume or cover letter that you would like to be considered under the 55-a Program. This position is also open to non 55-a Program candidates who meet the education and experience requirements as listed in the job posting notice.
5th rowThe Human Resources Administration/Department of Social Services offers competitive salaries and the following benefits: Generous Pension Plans (The New York Employees' Retirement System); 401(k) and 457 Roth's Retirement Savings Programs; U.S. Savings Bonds Flexible Spending Program; Health Benefits, Dental, Vision Coverage, Prescription Drug Program; Training and Professional Development; Opportunity for Scholarship; College Savings Program; Paid Holidays and Generous Annual Leave;
ValueCountFrequency (%)
the 3275
 
4.2%
to 3017
 
3.8%
of 2587
 
3.3%
and 2159
 
2.7%
for 1553
 
2.0%
be 1423
 
1.8%
a 1305
 
1.7%
you 1128
 
1.4%
program 883
 
1.1%
or 882
 
1.1%
Other values (1505) 60474
76.9%
2023-12-09T20:31:32.144496image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
81275
15.0%
e 49670
 
9.2%
i 36069
 
6.7%
o 34339
 
6.3%
t 33564
 
6.2%
n 29743
 
5.5%
a 29688
 
5.5%
r 28229
 
5.2%
s 23418
 
4.3%
l 18096
 
3.3%
Other values (86) 178166
32.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 397924
73.4%
Space Separator 81275
 
15.0%
Uppercase Letter 41205
 
7.6%
Other Punctuation 13315
 
2.5%
Decimal Number 2850
 
0.5%
Control 2665
 
0.5%
Dash Punctuation 977
 
0.2%
Close Punctuation 684
 
0.1%
Open Punctuation 676
 
0.1%
Currency Symbol 674
 
0.1%
Other values (3) 12
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 49670
12.5%
i 36069
 
9.1%
o 34339
 
8.6%
t 33564
 
8.4%
n 29743
 
7.5%
a 29688
 
7.5%
r 28229
 
7.1%
s 23418
 
5.9%
l 18096
 
4.5%
d 13751
 
3.5%
Other values (17) 101357
25.5%
Uppercase Letter
ValueCountFrequency (%)
P 4182
 
10.1%
A 3699
 
9.0%
S 3645
 
8.8%
E 3166
 
7.7%
T 2864
 
7.0%
N 2761
 
6.7%
C 2464
 
6.0%
R 2283
 
5.5%
O 2148
 
5.2%
D 1941
 
4.7%
Other values (17) 12052
29.2%
Other Punctuation
ValueCountFrequency (%)
. 4806
36.1%
, 4008
30.1%
/ 1315
 
9.9%
: 1132
 
8.5%
* 885
 
6.6%
; 723
 
5.4%
' 257
 
1.9%
& 135
 
1.0%
# 28
 
0.2%
! 12
 
0.1%
Other values (2) 14
 
0.1%
Decimal Number
ValueCountFrequency (%)
5 720
25.3%
0 447
15.7%
4 377
13.2%
1 355
12.5%
2 278
 
9.8%
7 235
 
8.2%
6 179
 
6.3%
3 152
 
5.3%
9 60
 
2.1%
8 47
 
1.6%
Control
ValueCountFrequency (%)
€ 1477
55.4%
™ 425
 
15.9%
333
 
12.5%
 260
 
9.8%
œ 149
 
5.6%
“ 11
 
0.4%
˜ 7
 
0.3%
” 2
 
0.1%
 1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 682
99.7%
] 2
 
0.3%
Open Punctuation
ValueCountFrequency (%)
( 674
99.7%
[ 2
 
0.3%
Currency Symbol
ValueCountFrequency (%)
¢ 623
92.4%
$ 51
 
7.6%
Space Separator
ValueCountFrequency (%)
81275
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 977
100.0%
Math Symbol
ValueCountFrequency (%)
+ 10
100.0%
Other Number
ValueCountFrequency (%)
½ 1
100.0%
Other Symbol
ValueCountFrequency (%)
© 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 439129
81.0%
Common 103128
 
19.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 49670
 
11.3%
i 36069
 
8.2%
o 34339
 
7.8%
t 33564
 
7.6%
n 29743
 
6.8%
a 29688
 
6.8%
r 28229
 
6.4%
s 23418
 
5.3%
l 18096
 
4.1%
d 13751
 
3.1%
Other values (44) 142562
32.5%
Common
ValueCountFrequency (%)
81275
78.8%
. 4806
 
4.7%
, 4008
 
3.9%
€ 1477
 
1.4%
/ 1315
 
1.3%
: 1132
 
1.1%
- 977
 
0.9%
* 885
 
0.9%
; 723
 
0.7%
5 720
 
0.7%
Other values (32) 5810
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 537816
99.2%
None 4441
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
81275
15.1%
e 49670
 
9.2%
i 36069
 
6.7%
o 34339
 
6.4%
t 33564
 
6.2%
n 29743
 
5.5%
a 29688
 
5.5%
r 28229
 
5.2%
s 23418
 
4.4%
l 18096
 
3.4%
Other values (72) 173725
32.3%
None
ValueCountFrequency (%)
€ 1477
33.3%
â 1477
33.3%
¢ 623
14.0%
™ 425
 
9.6%
 260
 
5.9%
œ 149
 
3.4%
“ 11
 
0.2%
˜ 7
 
0.2%
 4
 
0.1%
¿ 3
 
0.1%
Other values (4) 5
 
0.1%
Distinct577
Distinct (%)57.9%
Missing3
Missing (%)0.3%
Memory size466.8 KiB
2023-12-09T20:31:32.447561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length2682
Median length1071
Mean length418.772317
Min length5

Characters and Unicode

Total characters417516
Distinct characters96
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique479 ?
Unique (%)48.0%

Sample

1st rowFor City employees, please go to Employee Self Service (ESS), click on Recruiting Activities > Careers, and search for Job ID# 592901. For all other applicants, please go to www.nyc.gov/jobs and search for Job ID# 592901 or click Apply Now below. SUBMISSION OF A RESUME IS NOT A GUARANTEE THAT YOU WILL RECEIVE AN INTERVIEW. APPOINTMENTS ARE SUBJECT TO OVERSIGHT APPROVAL.
2nd rowApply online with a cover letter to https://a127-jobs.nyc.gov/. In the Job ID search bar, enter: job ID number # 595589. We appreciate the interest and thank all applicants who apply, but only those candidates under consideration will be contacted. The NYC Health Department is committed to recruiting and retaining a diverse and culturally responsive workforce. We strongly encourage people of color, people with disabilities, veterans, women, and lesbian, gay, bisexual, and transgender and gender non-conforming persons to apply. All applicants will be considered without regard to actual or perceived race, color, national origin, religion, sexual orientation, marital or parental status, disability, sex, gender identity or expression, age, prior record of arrest; or any other basis prohibited by law. NOTE: This position is open to qualified persons with a disability who are eligible for the 55-a Program. Please indicate in your resume that you would like to be considered for the position under the 55-a Program.
3rd row*******THE PROPOSED SALARY RANGE FOR THIS POSITION IS $ 66,430 - $76,394 YEARLY********** APPLICANTS MUST BE PERMANAENT IN THE ASSOCIATE JOB OPPORTUNITY SPECIALIST CIVIL SERVICE TITLE. Click Apply Now Button
4th rowCurrent NYC employees may apply to Job ID: 589409 via Employee Self Service (ESS): www.nyc.gov/ess. External applicants may visit the NYC Jobs website: www.nyc.gov/jobs and apply to Job ID: 589409. While all complete applications will be given consideration, only candidates selected for an interview will be contacted.
5th rowClick Apply Now button
ValueCountFrequency (%)
to 2671
 
4.2%
and 2107
 
3.3%
the 2006
 
3.2%
for 1776
 
2.8%
be 1276
 
2.0%
apply 1149
 
1.8%
or 1082
 
1.7%
job 1066
 
1.7%
id 954
 
1.5%
a 938
 
1.5%
Other values (1308) 48318
76.3%
2023-12-09T20:31:32.929760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
64769
15.5%
e 33716
 
8.1%
o 25527
 
6.1%
t 22913
 
5.5%
i 21651
 
5.2%
a 21496
 
5.1%
r 20776
 
5.0%
n 19437
 
4.7%
l 15967
 
3.8%
s 15550
 
3.7%
Other values (86) 155714
37.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 280481
67.2%
Space Separator 64769
 
15.5%
Uppercase Letter 49284
 
11.8%
Other Punctuation 14546
 
3.5%
Decimal Number 5724
 
1.4%
Dash Punctuation 1246
 
0.3%
Control 651
 
0.2%
Close Punctuation 343
 
0.1%
Open Punctuation 335
 
0.1%
Currency Symbol 84
 
< 0.1%
Other values (3) 53
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 33716
12.0%
o 25527
 
9.1%
t 22913
 
8.2%
i 21651
 
7.7%
a 21496
 
7.7%
r 20776
 
7.4%
n 19437
 
6.9%
l 15967
 
5.7%
s 15550
 
5.5%
c 11275
 
4.0%
Other values (17) 72173
25.7%
Uppercase Letter
ValueCountFrequency (%)
E 5180
 
10.5%
I 4412
 
9.0%
A 4014
 
8.1%
N 3974
 
8.1%
T 3963
 
8.0%
S 3597
 
7.3%
O 3217
 
6.5%
C 3047
 
6.2%
D 2475
 
5.0%
P 2365
 
4.8%
Other values (16) 13040
26.5%
Other Punctuation
ValueCountFrequency (%)
. 5121
35.2%
, 5115
35.2%
/ 1760
 
12.1%
: 1249
 
8.6%
# 707
 
4.9%
* 226
 
1.6%
; 213
 
1.5%
' 89
 
0.6%
@ 29
 
0.2%
& 28
 
0.2%
Other values (4) 9
 
0.1%
Decimal Number
ValueCountFrequency (%)
5 1460
25.5%
1 683
11.9%
2 603
10.5%
7 557
 
9.7%
6 521
 
9.1%
0 497
 
8.7%
9 462
 
8.1%
8 386
 
6.7%
3 319
 
5.6%
4 236
 
4.1%
Control
ValueCountFrequency (%)
€ 318
48.8%
 99
 
15.2%
œ 98
 
15.1%
™ 73
 
11.2%
50
 
7.7%
˜ 6
 
0.9%
“ 4
 
0.6%
 3
 
0.5%
Math Symbol
ValueCountFrequency (%)
> 29
80.6%
= 4
 
11.1%
+ 3
 
8.3%
Currency Symbol
ValueCountFrequency (%)
$ 46
54.8%
¢ 38
45.2%
Space Separator
ValueCountFrequency (%)
64769
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1246
100.0%
Close Punctuation
ValueCountFrequency (%)
) 343
100.0%
Open Punctuation
ValueCountFrequency (%)
( 335
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 16
100.0%
Other Number
ValueCountFrequency (%)
½ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 329765
79.0%
Common 87751
 
21.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 33716
 
10.2%
o 25527
 
7.7%
t 22913
 
6.9%
i 21651
 
6.6%
a 21496
 
6.5%
r 20776
 
6.3%
n 19437
 
5.9%
l 15967
 
4.8%
s 15550
 
4.7%
c 11275
 
3.4%
Other values (43) 121457
36.8%
Common
ValueCountFrequency (%)
64769
73.8%
. 5121
 
5.8%
, 5115
 
5.8%
/ 1760
 
2.0%
5 1460
 
1.7%
: 1249
 
1.4%
- 1246
 
1.4%
# 707
 
0.8%
1 683
 
0.8%
2 603
 
0.7%
Other values (33) 5038
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 416548
99.8%
None 968
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
64769
15.5%
e 33716
 
8.1%
o 25527
 
6.1%
t 22913
 
5.5%
i 21651
 
5.2%
a 21496
 
5.2%
r 20776
 
5.0%
n 19437
 
4.7%
l 15967
 
3.8%
s 15550
 
3.7%
Other values (74) 154746
37.1%
None
ValueCountFrequency (%)
€ 318
32.9%
â 318
32.9%
 99
 
10.2%
œ 98
 
10.1%
™ 73
 
7.5%
¢ 38
 
3.9%
 7
 
0.7%
˜ 6
 
0.6%
¿ 5
 
0.5%
“ 4
 
0.4%
Other values (2) 2
 
0.2%

hours_shift
Text

MISSING 

Distinct153
Distinct (%)38.8%
Missing606
Missing (%)60.6%
Memory size52.1 KiB
2023-12-09T20:31:33.282645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length221
Median length129
Mean length25.65228426
Min length3

Characters and Unicode

Total characters10107
Distinct characters77
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)25.4%

Sample

1st row9:00am-17:00pm (Flexible)
2nd row35 Hours/Day Shift
3rd row35 hours per week / day
4th rowM-F, 9-5
5th row35 hours weekly/Day
ValueCountFrequency (%)
hours 239
 
12.6%
35 218
 
11.5%
per 101
 
5.3%
68
 
3.6%
– 62
 
3.3%
week 57
 
3.0%
week/day 48
 
2.5%
to 44
 
2.3%
monday 38
 
2.0%
friday 38
 
2.0%
Other values (235) 988
52.0%
2023-12-09T20:31:33.807217image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1556
 
15.4%
e 649
 
6.4%
r 561
 
5.6%
s 511
 
5.1%
o 495
 
4.9%
a 404
 
4.0%
5 358
 
3.5%
u 346
 
3.4%
i 311
 
3.1%
0 298
 
2.9%
Other values (67) 4618
45.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5658
56.0%
Space Separator 1556
 
15.4%
Decimal Number 1218
 
12.1%
Uppercase Letter 845
 
8.4%
Other Punctuation 391
 
3.9%
Dash Punctuation 164
 
1.6%
Control 138
 
1.4%
Open Punctuation 64
 
0.6%
Close Punctuation 63
 
0.6%
Currency Symbol 4
 
< 0.1%
Other values (2) 6
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 649
 
11.5%
r 561
 
9.9%
s 511
 
9.0%
o 495
 
8.7%
a 404
 
7.1%
u 346
 
6.1%
i 311
 
5.5%
d 258
 
4.6%
h 252
 
4.5%
y 239
 
4.2%
Other values (15) 1632
28.8%
Uppercase Letter
ValueCountFrequency (%)
H 147
17.4%
M 134
15.9%
F 89
10.5%
T 63
 
7.5%
S 61
 
7.2%
D 40
 
4.7%
A 39
 
4.6%
P 39
 
4.6%
N 39
 
4.6%
W 33
 
3.9%
Other values (15) 161
19.1%
Decimal Number
ValueCountFrequency (%)
5 358
29.4%
0 298
24.5%
3 274
22.5%
9 111
 
9.1%
1 58
 
4.8%
4 40
 
3.3%
2 31
 
2.5%
7 18
 
1.5%
8 18
 
1.5%
6 12
 
1.0%
Other Punctuation
ValueCountFrequency (%)
: 146
37.3%
/ 129
33.0%
. 65
16.6%
, 41
 
10.5%
; 9
 
2.3%
* 1
 
0.3%
Control
ValueCountFrequency (%)
€ 66
47.8%
“ 64
46.4%
6
 
4.3%
™ 2
 
1.4%
Space Separator
ValueCountFrequency (%)
1556
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 164
100.0%
Open Punctuation
ValueCountFrequency (%)
( 64
100.0%
Close Punctuation
ValueCountFrequency (%)
) 63
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 4
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%
Other Number
ValueCountFrequency (%)
½ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6503
64.3%
Common 3604
35.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 649
 
10.0%
r 561
 
8.6%
s 511
 
7.9%
o 495
 
7.6%
a 404
 
6.2%
u 346
 
5.3%
i 311
 
4.8%
d 258
 
4.0%
h 252
 
3.9%
y 239
 
3.7%
Other values (40) 2477
38.1%
Common
ValueCountFrequency (%)
1556
43.2%
5 358
 
9.9%
0 298
 
8.3%
3 274
 
7.6%
- 164
 
4.6%
: 146
 
4.1%
/ 129
 
3.6%
9 111
 
3.1%
€ 66
 
1.8%
. 65
 
1.8%
Other values (17) 437
 
12.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9903
98.0%
None 204
 
2.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1556
 
15.7%
e 649
 
6.6%
r 561
 
5.7%
s 511
 
5.2%
o 495
 
5.0%
a 404
 
4.1%
5 358
 
3.6%
u 346
 
3.5%
i 311
 
3.1%
0 298
 
3.0%
Other values (61) 4414
44.6%
None
ValueCountFrequency (%)
â 66
32.4%
€ 66
32.4%
“ 64
31.4%
 3
 
1.5%
½ 3
 
1.5%
™ 2
 
1.0%

work_location_1
Text

MISSING 

Distinct149
Distinct (%)35.9%
Missing585
Missing (%)58.5%
Memory size54.8 KiB
2023-12-09T20:31:34.186136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length336
Median length122
Mean length32.78554217
Min length3

Characters and Unicode

Total characters13606
Distinct characters73
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)21.7%

Sample

1st rowNew York, NY
2nd row109 E 16Th St., N.Y.
3rd row5 Manhattan West
4th rowWards Island, N.Y.
5th row1 Metro Tech, Brooklyn Ny
ValueCountFrequency (%)
ny 303
 
12.1%
thomson 77
 
3.1%
30-30 75
 
3.0%
ave 74
 
2.9%
st 73
 
2.9%
street 62
 
2.5%
city 52
 
2.1%
york 49
 
2.0%
new 49
 
2.0%
water 49
 
2.0%
Other values (326) 1649
65.6%
2023-12-09T20:31:34.754020image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2110
 
15.5%
e 795
 
5.8%
o 708
 
5.2%
n 645
 
4.7%
t 616
 
4.5%
r 592
 
4.4%
a 582
 
4.3%
1 435
 
3.2%
0 415
 
3.1%
N 395
 
2.9%
Other values (63) 6313
46.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6679
49.1%
Uppercase Letter 2198
 
16.2%
Space Separator 2110
 
15.5%
Decimal Number 1927
 
14.2%
Other Punctuation 491
 
3.6%
Dash Punctuation 170
 
1.2%
Close Punctuation 11
 
0.1%
Open Punctuation 10
 
0.1%
Control 10
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 795
11.9%
o 708
10.6%
n 645
9.7%
t 616
 
9.2%
r 592
 
8.9%
a 582
 
8.7%
s 318
 
4.8%
l 304
 
4.6%
i 287
 
4.3%
y 270
 
4.0%
Other values (17) 1562
23.4%
Uppercase Letter
ValueCountFrequency (%)
N 395
18.0%
Y 281
12.8%
C 204
9.3%
S 156
 
7.1%
A 136
 
6.2%
T 130
 
5.9%
L 114
 
5.2%
B 107
 
4.9%
I 105
 
4.8%
W 104
 
4.7%
Other values (14) 466
21.2%
Decimal Number
ValueCountFrequency (%)
1 435
22.6%
0 415
21.5%
5 289
15.0%
3 273
14.2%
2 137
 
7.1%
6 103
 
5.3%
9 98
 
5.1%
7 64
 
3.3%
4 62
 
3.2%
8 51
 
2.6%
Other Punctuation
ValueCountFrequency (%)
, 368
74.9%
. 115
 
23.4%
/ 4
 
0.8%
: 2
 
0.4%
# 1
 
0.2%
& 1
 
0.2%
Control
ValueCountFrequency (%)
€ 5
50.0%
“ 5
50.0%
Space Separator
ValueCountFrequency (%)
2110
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 170
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8877
65.2%
Common 4729
34.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 795
 
9.0%
o 708
 
8.0%
n 645
 
7.3%
t 616
 
6.9%
r 592
 
6.7%
a 582
 
6.6%
N 395
 
4.4%
s 318
 
3.6%
l 304
 
3.4%
i 287
 
3.2%
Other values (41) 3635
40.9%
Common
ValueCountFrequency (%)
2110
44.6%
1 435
 
9.2%
0 415
 
8.8%
, 368
 
7.8%
5 289
 
6.1%
3 273
 
5.8%
- 170
 
3.6%
2 137
 
2.9%
. 115
 
2.4%
6 103
 
2.2%
Other values (12) 314
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13591
99.9%
None 15
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2110
 
15.5%
e 795
 
5.8%
o 708
 
5.2%
n 645
 
4.7%
t 616
 
4.5%
r 592
 
4.4%
a 582
 
4.3%
1 435
 
3.2%
0 415
 
3.1%
N 395
 
2.9%
Other values (60) 6298
46.3%
None
ValueCountFrequency (%)
â 5
33.3%
€ 5
33.3%
“ 5
33.3%

recruitment_contact
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1000
Missing (%)100.0%
Memory size7.9 KiB
Distinct36
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size306.6 KiB
2023-12-09T20:31:35.039729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length764
Median length400
Mean length256.876
Min length3

Characters and Unicode

Total characters256876
Distinct characters59
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)1.8%

Sample

1st rowNew York City residency is generally required within 90 days of appointment. However, City Employees in certain titles who have worked for the City for 2 continuous years may also be eligible to reside in Nassau, Suffolk, Putnam, Westchester, Rockland, or Orange County. To determine if the residency requirement applies to you, please discuss with the agency representative at the time of interview.
2nd rowNew York City residency is generally required within 90 days of appointment. However, City Employees in certain titles who have worked for the City for 2 continuous years may also be eligible to reside in Nassau, Suffolk, Putnam, Westchester, Rockland, or Orange County. To determine if the residency requirement applies to you, please discuss with the agency representative at the time of interview.
3rd rowNew York City Residency is not required for this position
4th rowNew York City residency is generally required within 90 days of appointment. However, City Employees in certain titles who have worked for the City for 2 continuous years may also be eligible to reside in Nassau, Suffolk, Putnam, Westchester, Rockland, or Orange County. To determine if the residency requirement applies to you, please discuss with the agency representative at the time of interview.
5th rowNew York City Residency is not required for this position.
ValueCountFrequency (%)
the 2331
 
5.7%
city 2088
 
5.1%
to 1745
 
4.3%
residency 1578
 
3.9%
for 1491
 
3.7%
of 1183
 
2.9%
in 1168
 
2.9%
is 934
 
2.3%
required 928
 
2.3%
york 908
 
2.2%
Other values (164) 26364
64.7%
2023-12-09T20:31:35.478943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
39725
15.5%
e 30722
12.0%
i 18938
 
7.4%
t 17868
 
7.0%
r 14463
 
5.6%
o 14047
 
5.5%
s 13319
 
5.2%
n 12972
 
5.0%
a 10673
 
4.2%
y 8346
 
3.2%
Other values (49) 75803
29.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 198541
77.3%
Space Separator 39725
 
15.5%
Uppercase Letter 10800
 
4.2%
Other Punctuation 6047
 
2.4%
Decimal Number 1755
 
0.7%
Open Punctuation 4
 
< 0.1%
Close Punctuation 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 30722
15.5%
i 18938
 
9.5%
t 17868
 
9.0%
r 14463
 
7.3%
o 14047
 
7.1%
s 13319
 
6.7%
n 12972
 
6.5%
a 10673
 
5.4%
y 8346
 
4.2%
l 6473
 
3.3%
Other values (14) 50720
25.5%
Uppercase Letter
ValueCountFrequency (%)
C 2757
25.5%
N 1580
14.6%
Y 994
 
9.2%
R 949
 
8.8%
H 646
 
6.0%
E 641
 
5.9%
T 622
 
5.8%
S 612
 
5.7%
O 606
 
5.6%
W 592
 
5.5%
Other values (12) 801
 
7.4%
Other Punctuation
ValueCountFrequency (%)
, 4123
68.2%
. 1911
31.6%
* 8
 
0.1%
: 2
 
< 0.1%
' 2
 
< 0.1%
/ 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
9 585
33.3%
0 585
33.3%
2 584
33.3%
8 1
 
0.1%
Space Separator
ValueCountFrequency (%)
39725
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 209341
81.5%
Common 47535
 
18.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 30722
14.7%
i 18938
 
9.0%
t 17868
 
8.5%
r 14463
 
6.9%
o 14047
 
6.7%
s 13319
 
6.4%
n 12972
 
6.2%
a 10673
 
5.1%
y 8346
 
4.0%
l 6473
 
3.1%
Other values (36) 61520
29.4%
Common
ValueCountFrequency (%)
39725
83.6%
, 4123
 
8.7%
. 1911
 
4.0%
9 585
 
1.2%
0 585
 
1.2%
2 584
 
1.2%
* 8
 
< 0.1%
( 4
 
< 0.1%
) 4
 
< 0.1%
: 2
 
< 0.1%
Other values (3) 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 256876
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
39725
15.5%
e 30722
12.0%
i 18938
 
7.4%
t 17868
 
7.0%
r 14463
 
5.6%
o 14047
 
5.5%
s 13319
 
5.2%
n 12972
 
5.0%
a 10673
 
4.2%
y 8346
 
3.2%
Other values (49) 75803
29.5%
Distinct272
Distinct (%)27.2%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
2023-12-09T20:31:35.773919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters23000
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique106 ?
Unique (%)10.6%

Sample

1st row2023-07-11T00:00:00.000
2nd row2023-07-31T00:00:00.000
3rd row2023-07-28T00:00:00.000
4th row2023-06-06T00:00:00.000
5th row2023-10-06T00:00:00.000
ValueCountFrequency (%)
2023-10-06t00:00:00.000 27
 
2.7%
2023-09-29t00:00:00.000 23
 
2.3%
2023-09-28t00:00:00.000 23
 
2.3%
2023-11-09t00:00:00.000 21
 
2.1%
2023-08-29t00:00:00.000 16
 
1.6%
2023-08-24t00:00:00.000 16
 
1.6%
2023-07-31t00:00:00.000 16
 
1.6%
2023-09-07t00:00:00.000 16
 
1.6%
2023-10-24t00:00:00.000 15
 
1.5%
2023-11-03t00:00:00.000 14
 
1.4%
Other values (262) 813
81.3%
2023-12-09T20:31:36.197742image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11277
49.0%
2 2580
 
11.2%
- 2000
 
8.7%
: 2000
 
8.7%
3 1079
 
4.7%
T 1000
 
4.3%
. 1000
 
4.3%
1 818
 
3.6%
9 298
 
1.3%
8 283
 
1.2%
Other values (4) 665
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17000
73.9%
Other Punctuation 3000
 
13.0%
Dash Punctuation 2000
 
8.7%
Uppercase Letter 1000
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11277
66.3%
2 2580
 
15.2%
3 1079
 
6.3%
1 818
 
4.8%
9 298
 
1.8%
8 283
 
1.7%
6 202
 
1.2%
7 181
 
1.1%
4 143
 
0.8%
5 139
 
0.8%
Other Punctuation
ValueCountFrequency (%)
: 2000
66.7%
. 1000
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 2000
100.0%
Uppercase Letter
ValueCountFrequency (%)
T 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22000
95.7%
Latin 1000
 
4.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11277
51.3%
2 2580
 
11.7%
- 2000
 
9.1%
: 2000
 
9.1%
3 1079
 
4.9%
. 1000
 
4.5%
1 818
 
3.7%
9 298
 
1.4%
8 283
 
1.3%
6 202
 
0.9%
Other values (3) 463
 
2.1%
Latin
ValueCountFrequency (%)
T 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11277
49.0%
2 2580
 
11.2%
- 2000
 
8.7%
: 2000
 
8.7%
3 1079
 
4.7%
T 1000
 
4.3%
. 1000
 
4.3%
1 818
 
3.6%
9 298
 
1.3%
8 283
 
1.2%
Other values (4) 665
 
2.9%

post_until
Text

MISSING 

Distinct104
Distinct (%)31.4%
Missing669
Missing (%)66.9%
Memory size43.0 KiB
2023-12-09T20:31:36.486820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters3641
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)12.7%

Sample

1st row28-NOV-2023
2nd row23-NOV-2023
3rd row16-DEC-2023
4th row14-FEB-2024
5th row21-DEC-2023
ValueCountFrequency (%)
05-jan-2024 14
 
4.2%
28-nov-2023 13
 
3.9%
27-dec-2023 11
 
3.3%
14-feb-2024 10
 
3.0%
23-nov-2023 9
 
2.7%
05-dec-2023 9
 
2.7%
03-dec-2023 8
 
2.4%
08-dec-2023 8
 
2.4%
21-feb-2024 7
 
2.1%
15-nov-2023 7
 
2.1%
Other values (94) 235
71.0%
2023-12-09T20:31:36.879599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 808
22.2%
- 662
18.2%
0 453
12.4%
3 230
 
6.3%
4 189
 
5.2%
E 158
 
4.3%
N 144
 
4.0%
1 128
 
3.5%
C 99
 
2.7%
D 95
 
2.6%
Other values (19) 675
18.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1986
54.5%
Uppercase Letter 993
27.3%
Dash Punctuation 662
 
18.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 158
15.9%
N 144
14.5%
C 99
10.0%
D 95
9.6%
A 91
9.2%
O 82
8.3%
V 78
7.9%
J 66
6.6%
B 59
 
5.9%
F 59
 
5.9%
Other values (8) 62
 
6.2%
Decimal Number
ValueCountFrequency (%)
2 808
40.7%
0 453
22.8%
3 230
 
11.6%
4 189
 
9.5%
1 128
 
6.4%
5 46
 
2.3%
8 45
 
2.3%
7 39
 
2.0%
6 29
 
1.5%
9 19
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 662
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2648
72.7%
Latin 993
 
27.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 158
15.9%
N 144
14.5%
C 99
10.0%
D 95
9.6%
A 91
9.2%
O 82
8.3%
V 78
7.9%
J 66
6.6%
B 59
 
5.9%
F 59
 
5.9%
Other values (8) 62
 
6.2%
Common
ValueCountFrequency (%)
2 808
30.5%
- 662
25.0%
0 453
17.1%
3 230
 
8.7%
4 189
 
7.1%
1 128
 
4.8%
5 46
 
1.7%
8 45
 
1.7%
7 39
 
1.5%
6 29
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3641
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 808
22.2%
- 662
18.2%
0 453
12.4%
3 230
 
6.3%
4 189
 
5.2%
E 158
 
4.3%
N 144
 
4.0%
1 128
 
3.5%
C 99
 
2.7%
D 95
 
2.6%
Other values (19) 675
18.5%
Distinct234
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
2023-12-09T20:31:37.172606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters23000
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)8.9%

Sample

1st row2023-07-11T00:00:00.000
2nd row2023-07-31T00:00:00.000
3rd row2023-07-28T00:00:00.000
4th row2023-08-08T00:00:00.000
5th row2023-11-06T00:00:00.000
ValueCountFrequency (%)
2023-11-09t00:00:00.000 31
 
3.1%
2023-10-06t00:00:00.000 26
 
2.6%
2023-09-29t00:00:00.000 25
 
2.5%
2023-10-24t00:00:00.000 24
 
2.4%
2023-11-03t00:00:00.000 20
 
2.0%
2023-11-08t00:00:00.000 17
 
1.7%
2023-10-17t00:00:00.000 17
 
1.7%
2023-10-30t00:00:00.000 17
 
1.7%
2023-09-28t00:00:00.000 17
 
1.7%
2023-09-11t00:00:00.000 16
 
1.6%
Other values (224) 790
79.0%
2023-12-09T20:31:37.583501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11256
48.9%
2 2530
 
11.0%
- 2000
 
8.7%
: 2000
 
8.7%
3 1112
 
4.8%
T 1000
 
4.3%
. 1000
 
4.3%
1 942
 
4.1%
9 328
 
1.4%
8 279
 
1.2%
Other values (4) 553
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17000
73.9%
Other Punctuation 3000
 
13.0%
Dash Punctuation 2000
 
8.7%
Uppercase Letter 1000
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11256
66.2%
2 2530
 
14.9%
3 1112
 
6.5%
1 942
 
5.5%
9 328
 
1.9%
8 279
 
1.6%
6 177
 
1.0%
7 168
 
1.0%
4 122
 
0.7%
5 86
 
0.5%
Other Punctuation
ValueCountFrequency (%)
: 2000
66.7%
. 1000
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 2000
100.0%
Uppercase Letter
ValueCountFrequency (%)
T 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22000
95.7%
Latin 1000
 
4.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11256
51.2%
2 2530
 
11.5%
- 2000
 
9.1%
: 2000
 
9.1%
3 1112
 
5.1%
. 1000
 
4.5%
1 942
 
4.3%
9 328
 
1.5%
8 279
 
1.3%
6 177
 
0.8%
Other values (3) 376
 
1.7%
Latin
ValueCountFrequency (%)
T 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11256
48.9%
2 2530
 
11.0%
- 2000
 
8.7%
: 2000
 
8.7%
3 1112
 
4.8%
T 1000
 
4.3%
. 1000
 
4.3%
1 942
 
4.1%
9 328
 
1.4%
8 279
 
1.2%
Other values (4) 553
 
2.4%

process_date
Text

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
2023-12-09T20:31:37.779727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters23000
Distinct characters9
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-11-14T00:00:00.000
2nd row2023-11-14T00:00:00.000
3rd row2023-11-14T00:00:00.000
4th row2023-11-14T00:00:00.000
5th row2023-11-14T00:00:00.000
ValueCountFrequency (%)
2023-11-14t00:00:00.000 1000
100.0%
2023-12-09T20:31:38.068182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 10000
43.5%
1 3000
 
13.0%
2 2000
 
8.7%
- 2000
 
8.7%
: 2000
 
8.7%
3 1000
 
4.3%
4 1000
 
4.3%
T 1000
 
4.3%
. 1000
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17000
73.9%
Other Punctuation 3000
 
13.0%
Dash Punctuation 2000
 
8.7%
Uppercase Letter 1000
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10000
58.8%
1 3000
 
17.6%
2 2000
 
11.8%
3 1000
 
5.9%
4 1000
 
5.9%
Other Punctuation
ValueCountFrequency (%)
: 2000
66.7%
. 1000
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 2000
100.0%
Uppercase Letter
ValueCountFrequency (%)
T 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22000
95.7%
Latin 1000
 
4.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10000
45.5%
1 3000
 
13.6%
2 2000
 
9.1%
- 2000
 
9.1%
: 2000
 
9.1%
3 1000
 
4.5%
4 1000
 
4.5%
. 1000
 
4.5%
Latin
ValueCountFrequency (%)
T 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10000
43.5%
1 3000
 
13.0%
2 2000
 
8.7%
- 2000
 
8.7%
: 2000
 
8.7%
3 1000
 
4.3%
4 1000
 
4.3%
T 1000
 
4.3%
. 1000
 
4.3%